Content uploaded by Dmitri Byzalov
Author content
All content in this area was uploaded by Dmitri Byzalov on Feb 17, 2014
Content may be subject to copyright.
Unbundling Cable Television: An Empirical Investigation
Dmitri Byzalovy
Temple University
November 2008
Abstract
I develop an empirical model of demand for large bundles, and use it to analyze bundling of
channels in cable television. Concerns over cable companies’bundling practices and rapid price
increases have led to a heated policy debate about government-mandated unbundling (requiring
cable companies to o¤er subscriptions to “themed tiers”or individual channels). I focus on the
likely short-run e¤ects of unbundling policies for consumers, networks and cable operators.
I model consumers’choice of cable/satellite packages (bundles of channels) and their subse-
quent viewing choices for individual channels in the bundle. The main identifying assumption
is that consumers’valuation of a bundle of channels is the utility they get from viewing those
channels. This allows me to identify consumers’dollar valuations for individual channels (even
though they are always sold in large bundles), and to predict their subscriptions and view-
ing choices in unbundling counterfactuals. I estimate the model using individual-level data on
cable/satellite subscriptions and viewership for 64 main cable channels.
I use the estimates to simulate the “themed tiers” scenario (breaking up the bundle into 7
mini-tiers by channel genre). I …nd that consumers do not gain much from unbundling. The
best-case increase in consumer surplus is estimated at just 35 cents per household per month.
Cable networks are likely to experience a sharp drop in the number of subscribers (but not
in viewership), which might force them to sharply increase the license fees they charge per
subscriber (in this case, unbundling would harm consumers).
I am especially grateful to Bharat Anand, Julie Mortimer and Ariel Pakes for their guidance and suggestions. I
would also like to thank Susan Athey, Ulrich Doraszelski, Gregory Lewis and Minjae Song, and seminar participants
at Harvard University for helpful discussions and comments. All errors are mine.
yDepartment of Accounting, Fox School of Business, Philadelphia, PA 19122, dmitri.byzalov@gmail.com
1
1. Introduction
I develop an empirical model of demand for large bundles, and use it to analyze the e¤ects of
bundling in cable television. A typical cable package is a bundle containing dozens of channels,
and most consumers only watch a small fraction of the channels they are paying for. For example,
FCC chairman Kevin Martin argues that “the average cable subscriber is paying for more than 85
channels that she doesn’t watch in order to obtain the approximately 16 channels that she does”
1.
Furthermore, cable companies keep adding more and more channels (the size of a typical cable
bundle more than doubled between 1995 and 2005), and the increases in cable prices consistently
outpace in‡ation2.
Concerns over rapid price increases and cable companies’bundling practices have led to a
heated policy debate in recent years, centering on the option of government-mandated unbundling
at the retail level (requiring cable companies to break up their main packages, allowing consumers
to pick individual channels or small mini-tiers on a la carte basis)3. Supporters of unbundling (the
FCC and various consumers’organizations) argue that it would signi…cantly bene…t consumers, by
reducing their cable bills and giving them more choice. On the other hand, opponents of unbundling
(most of the cable companies and programmers) argue that it would increase cable prices and
destroy the economic foundations of the cable networks, reducing the quality and diversity of
programming in the long run4. More than 80% of US households subscribe to cable or satellite,
and an average cable household spends more than 8 hours a day watching television (Nielsen [2006])
and more than $600 a year paying for it (FCC [2005a]), so a lot is at stake in this policy debate.
However, empirical evidence is scarce (the only empirical analysis of cable unbundling I am aware
of is a parallel paper by Crawford and Yurukoglu [2008], discussed later).
In the empirical analysis, I address two main questions. First, what are the likely e¤ects of
unbundling for consumers? The answer to this question is key to the debate, since the push for
unbundling is based on the argument that it will substantially bene…t consumers. Second, what are
the likely e¤ects for the industry? Speci…cally, how will it a¤ect prices, subscriptions, viewership
and pro…ts for cable operators and cable networks? Importantly, I focus on the short-run e¤ects, i.e.
I hold the set of available networks and the quality of their programming …xed. One major concern
about unbundling is that it can sharply reduce cable networks’ subscriber base and viewership
(their two main sources of revenue), which in the long run can destroy many niche networks and
force others to sharply cut their programming expenditures. Thus, the short-run outcomes for the
1Chicago Tribune, July 20, 2007. GAO (2003) also reports that households receiving over 70 networks watch on
average only 17 of them.
2From 1995 to 2005, the number of cable channels in a typical subscription increased from 24 to 64 (SNL Kagan
[2007]), and nominal cable prices increased by 93%, vs a 28% increase in the CPI (FCC [2006]).
3For example, in response to requests from members of Congress, the Federal Communications Commission (FCC)
and the Government Accountability O¢ ce (GAO) have published three reports (FCC [2004, 2006], GAO [2003])
analyzing the e¤ects of a switch to a la carte or mini-tiers, and FCC chairman Kevin Martin argued for unbundling in
numerous congressional hearings (e.g. November 2005, April 2007, April 2008). Notice that there is also a separate
(but closely-related) debate about “wholesale unbundling” in the upstream market for cable programming.
4Reports by FCC (2006) and Booz, Allen, Hamilton (2004) are representative of the two sides’ main arguments.
Although the two reports reach very di¤erent conclusions, both acknowledge lack of any serious empirical evidence
on the key variables driving their arguments (e.g. Booz, Allen, Hamilton [2004] page 19, FCC [2006] page 39).
2
networks can have important long-run welfare e¤ects.
Unbundling at the retail level is likely to dramatically alter the equilibrium in the wholesale
market for cable programming (see section 2.2 for details). While full analysis would require a
credible empirical model of the wholesale market (which is likely to be prohibitively complex5), I
use a simpler approach. Speci…cally, …rst I estimate a detailed model of consumers’ demand for
cable bundles and viewership, which allows me to predict their subscriptions and viewing choices
in unbundling counterfactuals. In counterfactuals, I compute cable operators’optimal retail pric-
ing decisions, treating the structure of their programming costs (license fees to the networks) as
exogenously given. I explore several alternative scenarios for the programming costs, which allows
me to bound the range of likely short-run e¤ects of unbundling.
The empirical model allows me to address an additional question. Speci…cally, how important
are the discriminatory e¤ects of bundling in my data? The price-discrimination theory of bundling
(e.g. Stigler [1963], Adams and Yellen [1976], Schmalensee [1984], McAfee et al [1989]) is one of
the main explanations for the widespread use of bundling, and cable television is often cited as a
natural example for this theory (e.g. Salinger [1995], Bakos and Brynjolfsson [1999]). However,
empirical evidence is scarce. In fact, the only empirical study I am aware of that focuses on the
discriminatory e¤ects of bundling is Crawford (2008). He presents reduced-form evidence for cable
television that o¤ers some support for the price-discrimination theory. In addition, several empirical
papers analyze other aspects of bundling. In particular, Chu, Leslie and Sorensen (2006) analyze
simple alternatives to mixed bundling for season tickets, Crawford and Yurukoglu (2008) analyze
the welfare e¤ects of unbundling policies in cable television, and Ho, Ho and Mortimer (2008)
analyze the e¤ects of full-line forcing contracts in the video rental industry.
Bundling is common in many markets (for example, software suites, season tickets, triple-
play bundles), and possible anticompetitive e¤ects of bundling have drawn a lot of attention from
researchers and policymakers6. Theoretical literature identi…es several e¤ects of bundling.
First, as mentioned above, bundling may have discriminatory e¤ects, facilitating surplus
extraction by the …rm. The magnitude of this e¤ect depends on the covariance structure of pref-
erences for the bundled goods, since the …rm can extract a greater fraction of the total surplus
if consumers’bundle valuations (the total for all the goods in the bundle) are less heterogeneous.
Bakos and Brynjolfsson (1999) show that this e¤ect is likely to be particularly strong for large
bundles (such as cable packages). Depending on the covariance structure of preferences and the
marginal costs of the bundled goods, bundling can be more or less pro…table than unbundled sales,
and in either case the implications for consumer surplus and total welfare are ambiguous7.
5Speci…cally, it would have to realistically capture bargaining between large programmers (such as Disney) and
large cable operators (such as Comcast), in which both sides have substantial market power and negotiate complex
multi-year multi-channel deals.
6For example, the …rst theoretical exposition of discriminatory e¤ects of bundling, Stigler (1963), was inspired by
antitrust cases focusing on block-booking of movies. Recent high-pro…le examples include the Microsoft case and the
antitrust review of the GE/Honeywell merger proposal.
7Speci…cally, besides redistributing surplus between consumers and the …rm, bundling ine¢ ciently excludes some
consumers who are served in the unbundled case, but on the other hand it serves some consumers who are ine¢ ciently
excluded in the unbundled case (e.g. Adams and Yellen [1976]).
3
Second, bundling may have entry-deterrence or leverage e¤ects (e.g. Whinston [1990], Nale-
bu¤ [2000, 2004], Bakos and Brynjolfsson [2000]). There are widespread concerns about entry-
deterrence and leverage e¤ects in the upstream programming market (competition among cable
channels)8. As for the retail level of the industry (the main focus of my analysis), entry-deterrence
does not seem to apply9. Leverage e¤ect of bundling is unlikely to be important at the retail
level, for two reasons. First, even though there is some exclusive sports programming (see section
2.2), most cable programming is available to all market participants. Second, for cable television,
the leverage mechanism (forcing consumers who value the exclusive good to get other goods from
the same …rm) does not require bundling. Notice that, with or without bundling, consumers are
unlikely to combine (say) Comcast and DirecTV, because doing so would double their equipment
charges and possibly other fees. Thus, while exclusive programming might give Comcast a compet-
itive advantage against DirecTV (which my demand model allows to capture), the role of bundling
in leveraging this advantage is minor.
Third, bundling may provide e¢ ciency bene…ts such as economies of scale or scope, or sim-
pler, more convenient choices for consumers (see Nalebu¤ [2003] for a review). For cable television,
the main e¢ ciency bene…ts of bundling are lower equipment and customer-service costs10. Also,
as mentioned earlier, retail unbundling will signi…cantly a¤ect the wholesale market for cable pro-
gramming. For reasons discussed in section 2.2, the wholesale prices (networks’ license fees per
subscriber) are likely to increase a lot after unbundling (one can think of this as a form of economies
of scale/scope due to bundling).
Unbundling can bene…t consumers in several ways (in the short run). If the discriminatory
e¤ects of bundling are strong, unbundling may signi…cantly reduce cable operators’ability to extract
surplus, resulting in a transfer of surplus from cable operators to consumers. Also, depending on
how it a¤ects the total cost of cable programming, it may result in a transfer of surplus from
networks to consumers (or to cable operators). Besides redistribution of surplus, unbundling may
increase total surplus, by partially serving consumers who are ine¢ ciently excluded under bundling
(e.g. current non-subscribers who value ESPN at above its unbundled price). On the other hand,
it may reduce total surplus, by ine¢ ciently excluding some of the current bundle buyers (e.g. those
who value ESPN at above zero but below its unbundled price), and by increasing equipment and
customer-service costs. The combined e¤ect of these factors is ambiguous, making it an empirical
question.
The main challenge in the empirical analysis is identifying consumers’valuations for individ-
ual channels. In order to predict the outcomes in unbundling counterfactuals and to characterize
the e¤ects of bundling, I need to estimate how much consumers are willing to pay for each channel
8For example, Consumers Union outlines possible anticompetitive e¤ects in its FCC …ling (Aug 13, 2004), available
at hearusnow.org/cablesatellite/5/.
9In the past 15 years there was successful entry by DirecTV and Dish, and (so far) successful entry by Verizon
FiOS and AT&T U-Verse. Thus, entry barriers created by bundling (if there are any) are not insurmountable.
10 Unbundling would require much wider deployment of digital set-top boxes, capable of blocking ‡exible combina-
tions of channels. However, cable operators are gradually switching to all-digital networks anyway (e.g. Multichannel
News, June 26, 2008), so this factor is becoming less important. Also, unbundling is likely to increase the number
and length of calls at cable operators’call centers, driving up the customer-service costs.
4
separately (including the covariance structure of channel valuations, since it is driving the discrim-
inatory e¤ects of bundling). However, most cable channels are always sold in bundles containing
dozens of channels (the only exception is premium channels like HBO), i.e. I do not observe any
unbundled sales for channels like CNN or ESPN. Thus, while consumers’bundle choices reliably
identify their valuations for entire bundles, I need a way to break them down into channel valuations.
My identi…cation strategy is based on combining data on consumers’ purchases (of entire
bundles) with additional data on their viewing choices (for individual channels). The fundamental
assumption is that consumers subscribe to cable because they want to watch cable. Thus, their
valuation of a bundle of channels is the utility they get from watching those channels, and channel
utilities can be identi…ed from their viewing choices. Notice that the viewing data allows me to
identify the joint distribution of valuations for individual channels even though consumers always
purchase entire bundles. Next, bundle utility is the result of explicit utility maximization over the
channels in the bundle. This links bundle utility to channel utilities in a fully structural, internally-
consistent way. By combining it with consumers’bundle choices and prices, I can link bundle utility
to dollars11.
I develop a structural empirical model in which I jointly model consumers’choice of a bundle
of channels and their viewing choices given the bundle. Notice that consumers self-select into
di¤erent bundles depending on their unobserved viewing preferences, therefore it is important to
model their viewing and bundle choices jointly, in order to account for this self-selection12. The
viewership part of the model is rooted in a standard random-utility discrete-choice framework,
which allows me to account for substitution among channels. Cable bundles contain dozens of
channels competing for consumers’ limited time, so substitution is likely to be important. For
example, the contribution of CNN to the value of a bundle depends on whether it also includes Fox
News and MSNBC. By linking bundle utility to channel utilities via explicit utility maximization,
I can fully account for such interactions.
I estimate the model using individual-level data from Simmons Research, which includes
consumers’viewing choices for 64 main cable channels and their cable/satellite subscriptions, com-
bined with several additional sources of data. Compared to more widely-available market-level
data, individual-level data provides several important advantages. First, since I directly observe
subscriptions and viewing choices for the same household, I can account for consumers’self-selection
into di¤erent bundles based on their viewing preferences. Second, I directly observe which channels
tend to be chosen by the same individual, and I directly observe viewing choices by multiple indi-
viduals within the same household. This allows me to accurately identify the covariance structure
of preferences (which drives the discriminatory e¤ects of bundling), both across channels and across
household members. In all cases, the main advantage of individual-level data is that it allows one
11 The same approach was used earlier by Ho (2006). She estimates demand for managed care plans, which o¤er
(among other things) access to a network of hospitals. She measures the contribution of each hospital to the value of
the plan for consumers, using data on their hospital choices. Crawford and Yurukoglu (2008) use the same approach.
12 For example, supp ose that those with higher viewing preferences are also more likely to subscribe to cable. If this
self-selection is ignored in estimation, the model would overpredict the utility of cable television for non-subscribers.
This would lead to a severe upward bias in predictions for unbundling policies, which make cable television more
a¤ordable to current non-subscribers.
5
to directly observe the empirical joint distribution of various outcomes. In contrast, even with
very detailed market-level data, one only observes the marginal distributions, separately for each
outcome. As a result, the identi…cation of self-selection patterns and covariances in market-level
data has to rely heavily on functional form assumptions.
A closely-related, parallel paper by Crawford and Yurukoglu (2008) focuses on the same main
questions and uses a similar identi…cation strategy. The main di¤erences between our papers are
driven by the di¤erences in the data13. I use individual-level data, and Crawford and Yurukoglu
use market-level data (local ratings and market shares for a large number of markets). As discussed
above, the main advantages of individual-level data are more accurate estimates of the covariance
structure of preferences and proper control for self-selection14 . On the other hand, Crawford and
Yurukoglu’s data covers a larger number of markets over multiple years, which provides richer
variation in the characteristics of cable packages. Thus, both papers have data-driven advantages
and disadvantages, and …ll important gaps in the empirical analysis.
I use the estimates to simulate the (short-run) outcomes of unbundling policies. My main
unbundling counterfactual is “themed tiers”, in which the cable bundle is broken up into seven mini-
tiers by channel genre15. I compute the outcomes for retail prices, subscriptions and viewership for
several alternative scenarios on cable operators’programming costs (the license fees they pay to
the networks).
As mentioned earlier, the push for unbundling is based on the argument that it will substan-
tially bene…t consumers. I …nd that consumers do not gain much from unbundling. Even if cable
networks do not increase their license fees per subscriber after unbundling, the gain in consumer
surplus is just 35 cents per household per month. However, networks’ license-fee revenues drop
substantially in this case, because consumers are no longer forced to subscribe to the networks they
do not value. If the networks increase their license fees per subscriber to try to o¤set this revenue
loss, consumer end up being worse o¤ than they were under bundling16.
In all cases, the networks lose subscribers, with dramatic losses (over two thirds) for some
mini-tiers. This is not surprising, since most consumers only watch a small fraction of the channels
they receive under bundling. Despite the loss of subscribers, in most cases total viewership does
not decline, and many mini-tiers actually gain viewers. This is important for the networks, since
advertising accounts for about half of their revenues. The e¤ect of unbundling on total industry
pro…ts is moderate. However, some scenarios result in substantial redistribution from the networks
to cable operators (but not to consumers).
13 Another potentially important di¤erence is in how we model the substitution patterns among channels. I capture
them in a fully structural way, while Crawford and Yurukoglu use elegant approximations.
14 In principle, it is possible to account for self-selection in market-level data (with heavy functional-form assump-
tions), however the computational cost in practice is prohibitive.
15 This is one of the main unbundling scenarios in FCC (2006). Another widely-discussed option is unbundling to
the level of individual channels. For reasons discussed in section 7, I use the mini-tiers as my primary scenario.
16 One alternative scenario that bene…ts consumers (without hurting the networks) is to combine unbundling with a
more e¢ cient arrangement in the upstream market (revenue-sharing or lump-sum payments to the networks, instead
of the current fee-per-subscriber arrangements that lead to double-marginalization). In this case, consumer surplus
increases by $1.63-$1.90 per household per month. However, these gains are not due to unbundling (a switch to a
similar upstream arrangement under bundling would yield even higher gains in consumer surplus).
6
Surprisingly, I …nd that there are no strong discriminatory e¤ects of bundling. In other
words, bundling does not facilitate surplus extraction by cable operators, relative to unbundled
sales. The most likely reason is that consumers’bundle valuations are still quite heterogeneous,
which constrains cable operator’s ability to extract surplus via bundling. The lack of discriminatory
e¤ects may explain why consumers do not gain much from unbundling: cable operators can extract
surplus from them equally e¤ectively using unbundled sales.
In the next section, I discuss relevant industry background. In section 3, I present a simpli…ed
version of the model, to illustrate the logic of my approach. Sections 4-7 present the data, empirical
speci…cation, estimation and empirical results.
2. Industry Background
I focus on the subscription television industry in years 2003-2004 (before entry by FiOS and U-
verse, and before the triple-play bundles). I discuss the retail level of the industry …rst, and after
that the upstream interactions between cable/satellite operators and other players in the market.
2.1. Retail Level
There are several ways to receive television programming: local antenna, cable and satellite. In
2004, 16% of TV households in the US used local antenna, 65% subscribed to cable and 19% to
satellite (FCC [2005a]). Local antenna reception is free, but it only provides access to the local
broadcast channels (ABC, CBS, NBC, FOX, etc), and the quality of reception is often low. Unlike
broadcast channels, cable channels (such as CNN or ESPN) are only available on subscription basis,
on cable and satellite. In the major metropolitan areas I focus on, availability of cable television is
close to 100%17 . Satellite television is available everywhere in the US, however its availability varies
within each area (and even within the same building) due to physical line-of-sight limitations18.
Most areas are served by one cable operator19. Cable operators o¤er several packages (tiers)
of TV channels, typically basic,expanded-basic and digital-basic packages20. The general structure
of cable packages is similar everywhere, but there is a lot of variation in prices and channel lineups
across locations, illustrated in section 4.2.
Basic package contains the local broadcast channels, and cable operators usually add a few
cable channels. Its price is usually regulated by the local franchise authorities. Other packages and
services are not regulated. Cable operators usually o¤er two additional packages, expanded-basic
17 National Cable and Telecommunications Association estimates that about 95% of all residences in the US are
wired for cable (FCC [2005a]). Most of the remaining 5% are probably in rural areas.
18 Satellite reception requires an unobstructed direct line of sight from the satellite to the dish antenna on the
customer’s house. Thus, satellite availability depends on the latitude and terrain, and within the same area, it is
lower in apartment buildings and for renters (see Goolsbee and Petrin [2004] for details).
19 The only exception is several “overbuild”communities with two competing cable operators, a dominant incumbent
and a relatively recent entrant (“overbuilder”). However, such communities account for just 3.1% of cable subscribers
in the US, and overbuilders get less than a …fth of those 3.1% (FCC 2005b).
20 Entry-level “family”packages were introduced later. Some cable systems also o¤er digital mini-tiers of additional
foreign-language channels, movie channels or sports channels.
7
and digital-basic. Expanded-basic package contains the main cable channels (CNN, ESPN, MTV,
TNT, etc), and it is usually the largest, most expensive package. Digital-basic package contains
additional, mostly niche channels. As of January 2004, the average prices were $18.08 a month
for basic cable, $27.24 for expanded-basic (on top of the basic price), with 44.6 cable channels on
average, and $16.05 for digital-basic (on top of the other two packages), with 31.6 extra channels
on average (FCC [2005b]). All cable subscribers have to get the basic package, and 88% of them
also subscribe to expanded basic, and 35% to digital basic (FCC [2005b]). In addition, consumers
can subscribe to premium channels like HBO, o¤ered on a la carte basis and priced at about $12
a month on average (per channel)21.
The main alternative to cable is satellite (DirecTV and Dish)22. Satellite operators o¤er sev-
eral base packages (roughly equivalent to digital cable in terms of channel lineups), plus premium
channels and several special-interest mini-tiers (foreign-language programming, additional sports
channels, etc). Unlike cable, there is no regional variation in prices and channel lineups for satellite
(except for the local broadcast channels, which by 2003-2004 were available in all major markets).
Besides the subscription fees, another potentially important cost for satellite subscribers is instal-
lation and equipment (satellite dish and receiver). However, in 2003-2004 satellite operators were
o¤ering free installation and equipment in exchange for a one-year commitment (FCC [2005a]).
2.2. Industry Structure and Contracts
The main players I focus on are the cable networks and cable/satellite operators. Cable networks
create their own programming or purchase it from studios, and deliver it to cable/satellite operators
via a satellite uplink. Cable and satellite operators bundle the networks into retail packages and
distribute them to consumers.
Cable networks have two main sources of revenue, license fees from cable/satellite operators
and advertising (on average, each is about half of the total). Most of the advertising time belongs
to the networks, while cable operators get about 2 minutes per hour for local advertising.
Carriage agreements between the networks and cable operators are negotiated on long-term
basis (up to 10 years). The contract speci…es the license fee per subscriber, and often also the tier
on which the network will be carried. The highest license fees are for the major sports networks, for
example ESPN is $2.28 per subscriber per month, and FOX Sports is $1.34/sub (2004 data from
SNL Kagan [2007])23. The most expensive non-sports channels are TNT ($0.82), Disney Channel
($0.76), USA ($0.44) and CNN ($0.43), while other major channels range between $0.10-$0.34/sub
per month, and most “fringe”channels are under 10 cents per subscriber.
21 Consumers have to get basic cable (but not higher tiers) in order to be able to subscribe to premium channels.
Many cable systems also o¤er a “multiplexed” version of the premium channels (e.g. “multiplexed” HBO is a mini-
package containing HBO, HBO2, HBO Family and HBO Signature).
22 The market share of other satellite providers (Voom, older large-dish systems) is negligible. Verizon FiOS and
AT&T U-verse entered the market later.
23 These are average license fees for each channel. The license fees likely vary across cable operators depending on
their bargaining p ower and speci…c terms of the contracts (which are highly proprietary).
8
Several large media companies own most of the cable networks24, and carriage agreements
are typically negotiated as a package deal for multiple networks. Thus, even though the license
fees in the data (SNL Kagan [2007]) are quoted separately for each network, actual agreements are
usually for wholesale bundles of networks. Furthermore, the wholesale bundling requirements in
these agreements appear to be practically important25 .
Notice that unbundling at the retail level is likely to dramatically change the wholesale
market for cable programming. Several factors may lead to sharp increases in networks’license
fees per subscriber. First, the subscriber counts for most channels will probably drop a lot, since
consumers will no longer be forced to get channels they do not want alongside the ones they want.
Second, networks might have to increase their marketing expenditures a lot, in order to attract
and retain subscribers26 . The e¤ect of unbundling on networks’ratings and advertising revenues is
ambiguous. On the one hand, it will likely eliminate most of the occasional viewers for each channel.
On the other hand, some channels may gain viewers, if unbundling reduces the number of other
channels consumers subscribe to, or if it attracts additional subscribers to cable. The change in the
audience composition may also increase or reduce the advertising rates per rating point27 . Several
additional factors may help o¤set the increases in the license fees. Speci…cally, retail unbundling
will change the nature of wholesale price competition among the networks, by eliminating wholesale
bundling of channels by large media companies, and by forcing the networks to directly compete
for subscribers (in addition to existing competition for viewers and advertisers)28 . These changes
may result in a more competitive wholesale market for cable programming. While the total e¤ect
of retail unbundling on the license fees is highly uncertain, opponents and supporters of unbundling
(e.g., Booz, Allen, Hamilton [2004] and FCC [2006]) agree that the fees per subscriber are likely
to go up (but they disagree on how much they will go up). In addition to the short-run e¤ects,
unbundling is likely to have major long-run e¤ects for networks’ entry, exit and investment in
programming, with important welfare implications in the long run29.
The main source of revenue for cable operators is monthly subscription fees for television
services ($50.63 per subscriber, 67% of the total revenue), plus they get additional revenues from
their share of advertising time ($4.60 per subscriber, 6% of the total) and other services, such
24 For example, among the 64 cable networks in my viewing data, 43 (67%) are owned by the 5 largest media
companies (Disney, NBC Universal, News Corporation, Time Warner, Viacom). The same companies own the 4
main broadcast networks (ABC, CBS, NBC, FOX).
25 For example, there was a well-publicised dispute between Echostar (Dish) and Viacom/CBS in 2004 that centered
on wholesale bundling by Viacom (e.g. Multichannel News, March 8, 2004). The American Cable Association also
argues that wholesale bundling forces them to carry a lot of costly undesired programming in order to get access to
the desired programming (americancable.org).
26 For example, the marketing expenditures for premium networks (sold a la carte) are between 15-25% of sales, vs
2-6% for the expanded-basic networks sold as a bundle (Booz, Allen, Hamilton [2004]).
27 For example, if advertisers value the total reach of their advertising (the number of unique viewers reached at
least once), then the exclusion of occasional viewers can reduce the advertising rates per rating point. On the other
hand, if advertisers value the ability to reach a well-de…ned niche audience, advertising rates can increase.
28 Under bundling, networks compete to gain carriage on cable systems, or to be placed on a more popular tier by
the cable operator, but they do not directly compete with each other for subscribers.
29 Opponents and supporters of unbundling o¤er widely divergent long-run pro jections, ranging from large declines
in quality or complete destruction of many networks (Booz, Allen, Hamilton [2004]) to emergence of a more vibrant
and diverse programming market (FCC [2006]).
9
as phone, internet30, video-on-demand, installation and equipment (27% of the total revenue; all
revenue numbers are national averages for 2004 from FCC [2005a]). License fees to the networks
are by far the largest part of cable operators’ marginal costs per subscriber ($15.95/month per
subscriber on average, FCC [2005a]). From conversations with industry executives, they treat most
other expenditures (including customer service) as …xed costs.
Several large cable operators are vertically integrated with cable networks. The most ex-
tensive one is Time Warner (the owner of Time Warner Cable), which fully owns CNN, Cinemax,
HBO, TBS, TNT, and 24 other national cable channels. Other large cable operators own relatively
few major channels31 . Vertically-integrated cable operators are required to make their channels
available to competitors on reasonable terms, and exclusive contracts are generally not allowed
(FCC [2005a]), although there are some exceptions32.
3. Basic Model Speci…cation
In this section, I present a simpli…ed version of the empirical model, to illustrate the general logic
of my approach. For clarity, I keep it as simple as possible, and defer most of the practical details
to the empirical speci…cation in section 5.
The model is a two-stage model of demand for cable/satellite bundles and TV-viewing. In the
…rst stage, I model household’s decision to subscribe to a bundle of TV channels. Notice that by a
“bundle of TV channels”(or “TV bundle”for brevity), I mean the combination of all packages and
premium channels the household subscribes to, for example “basic”+“expanded-basic”+“HBO”
(in unbundling counterfactuals, each possible combination of channels or mini-tiers is treated as
a separate TV bundle). In the second stage, I model the TV-viewing choices for each individual
within the household, conditional on the TV bundle.
The key assumption is that consumers’bundle subscription choices in stage 1 are driven by
the utility they expect to get from watching the channels in the bundle in stage 2. Thus, stage
2 identi…es the utility they get from watching each channel, while stage 1 links bundle choices to
viewing utility and prices. The time frame of the model is dictated by the structure of my data (a
cross-section of total viewing times for each channel last week, for each individual).
It is more convenient to present the model backwards.
3.1. Stage 2: TV-viewing Conditional on the TV bundle
30 Triple-play bundles were introduced later, but by 2003-2004 cable companies were o¤ering broadband internet in
most markets, and phone services in relatively few markets.
31 Other large vertically-integrated operators are Cablevision, Comcast and Cox. They own some of the ma jor
regional sports networks and local news channels, but few of the major national networks. For example, among
the 64 national cable channels in my viewing data, Cablevision, Comcast and Cox combined have partial ownership
in 13 channels, and these are mostly “fringe” channels (they account for less than 12% of the total cost of cable
programming).
32 Speci…cally, DirecTV has exclusive rights to NFL Sunday Ticket, and cable operators have exclusive rights to
regional sports networks in some markets (FCC [2005a]). These exclusive deals exploit loopholes in the regulation.
10
Household hsubscribes to bundle Sh(list of channels). Household members i= 1:::Khhave
observed characteristics Xh;i and unobserved (to the researcher) characteristics wh;i. There are T
periods per week33. In each period t, individual ican choose to watch one of the channels in the
bundle (j2Sh)or the outside alternative j= 0.
Her utility from watching channel j2Shin period tfollows a standard random-coe¢ cients
speci…cation
Uh;i;j;t =j+Zjh;i +"h;i;j;t
where jis the vertical characteristic of channel j,Zjare its horizontal characteristics, h;i are
individual i’s preferences, and "h;i;j;t are i.i.d. logit (type-I extreme value) shocks. The preferences
are speci…ed as
h;i =Xh;i +wh;i
The unobserved component of preferences is speci…ed as wh;i =ewh+ewh;i ;with ewh;i sN(0;)
and ewhsN(0; );where determines the correlation in unobserved preferences across household
members34 :Matrices ; and scalar are free parameters in estimation.
The utility from the outside alternative j= 0 is normalized to Uh;i;0;t = 0 + "h;i;0;t, where
"h;i;0;t is an i.i.d. logit shock.
Determinants of the discriminatory e¤ects
Discriminatory e¤ects of bundling are driven by the covariance structure of preferences (across
channels for each individual and across individuals within each household), which in turn is deter-
mined by the parameters ; and ; channel characteristics Zjand the distribution of demographics
Xh;i in the population. For example, if consumers’preferences for sports and family channels are
strongly negatively correlated (via either or ), then their valuations for a bundle of sports and
family channels will be less heterogeneous, allowing the …rm to extract surplus more e¤ectively.
This may make bundling more pro…table than unbundled sales (exact answer depends in a complex
way on the joint distribution of channel valuations, so the only way to get a de…nitive answer is to
compute the optimal prices and pro…ts for both cases).
Notice that the covariances across household members are also important. For example,
suppose that a typical household consists of two individuals, one of which likes sports channels but
not family channels, and the other has reverse preferences. In this case, even though the valuations
for each group of channels are heterogeneous across individuals, they are much less heterogeneous
across households. Thus, the …rm can extract surplus e¤ectively using unbundled sales, and the
heterogeneity-reduction advantage of bundling might become less important.
Since cable subscription is a household-level decision, I could simplify the model (and espe-
cially the estimation) by directly modeling household-level viewing preferences (after aggregating
the viewing data from individual to household level). However, by explicitly modeling viewing
choices for each household member, I can capture the covariance structure of channel valuations
33 For simplicity, I treat all time periods the same. With more detailed data, the model can be easily extended to
accommodate di¤erences across shows or day parts (e.g. daytime vs primetime).
34 The covariance matrices of ewhand ewh;i do not have to be proportional to each other. The only reason I impose
this assumption is to reduce the number of parameters.
11
(for entire households) much more accurately. For example, suppose that the main determinants
of viewing preferences (at the individual level) are gender and age. Then, the covariance structure
of household-level channel valuations is likely to be radically di¤erent for di¤erent household types
(e.g. a married couple of similar ages vs a married couple of dissimilar ages vs a single household).
By modeling viewing choices at the individual level, I can accurately capture the covariance pat-
terns for di¤erent household types, in a simple and transparent way. On the other hand, if one were
to model viewing choices directly at the household level, it would require a much more complicated
reduced-form speci…cation (to account for every possible combination of household members).
Expected utility from TV-viewing for bundle Sh
In each period, the individual chooses the alternative that maximizes her utility, among the
channels in Shand the outside alternative. Thus, her realized ex-post utility in period tis equal to
maxfUh;i;j;tgj2fSh;0g
Before the draws of the shocks "h;i;j;t for period thave been realized, her expected utility for period
tis
EmaxfUh;i;j;tgj2fSh;0g
where the expectation is over the draws of the "h;i;j;t-s:This is the expected viewing utility relevant
at the bundle choice stage. This implicitly assumes that consumers have perfect information about
channel characteristics j; Zjfor all the channels in the bundle35. Notice that the unobserved
characteristics wh;i are systematic and known to consumers, so they are not absorbed in this
expectation. Thus, the only uncertainty (at the bundle choice stage) is with respect to the future
draws of the shocks "h;i;j;t36 . Also, since the systematic part of channel utilities is assumed to be
the same for all t-s, the expected utility from TV-viewing is also the same for all t-s.
For logit shocks, this expected utility has a simple analytical expression (Ben-Akiva [1973])
EU (ShjXh;i; wh;i ) = Emaxfj+Zjh;i +"h;i;j;t gj2fSh;0g=
= ln 0
@X
j2fSh;0g
exp j+Zjh;i1
A(3.1)
Notice that the expected utility for the bundle is not additive with respect to channel utilities,
which is a natural implication of the random-utility discrete-choice framework. The reason is that
di¤erent channels are substitutes for each other at any given moment. Thus, when a new channel
jis added to the bundle, its contribution to bundle utility depends on the other channels in the
35 Notice that this assumption is standard in discrete-choice models of demand. In particular, most empirical papers
assume that consumers have perfect information about the main characteristics of all the alternatives in the choice
set, even if it contains hundreds of products (e.g. cars in Berry, Levinsohn and Pakes [1995]).
36 I assume that all the unobservables in channel utilities are either systematic (wh;i) or completely idiosyncratic
("h;i;j;t):With more detailed data, the model can be extended to accommodate a more ‡exible covariance structure of
the shocks, e.g. I could allow for somewhat persistent shocks in viewing preferences (such shocks would be absorbed
in the expectation).
12
bundle.
Given the estimates of channel utilities, I can compute expected viewing utility for any bundle
of channels (and not just the bundles I observe in the data). Notice that it is well-de…ned for any
combination of the available channels, since it is simply E(max)for several random variables with
a known joint distribution. This feature is crucial for the unbundling counterfactuals, in which I
have to evaluate utility from new bundles never observed in the data37.
3.2. Stage 1: Bundle Subscription Choice
Household hchooses a bundle from a menu of all available TV bundles (local antenna, various
combinations of cable or satellite packages and premium channels38). The subscription-stage utility
from bundle S(list of channels) at price Pis speci…ed as
U(S; P jXh; wh) = F(EU1; :::; EUKh) + (Xh; wP
h)P(3.2)
where EUiEU (SjXh;i; wh;i )is individual i’s expected viewing utility for bundle S,F(:::)is a
function that aggregates household members’viewing utilities39,Xh(Xh;1; :::; Xh;Kh)and wh
(wh;1; :::; wh;Kh)are the observed and unobserved characteristics for all the household members
i= 1:::Kh, and (Xh; wP
h)is the price coe¢ cient that varies across households depending on their
observables Xh(e.g. income) and unobservable wP
h.
Given the menu of all available bundles, the household chooses the bundle that yields the
highest utility. After integrating out the unobservables, this yields predicted probabilities for all
cable and satellite bundles. Notice that given the estimates of channel utilities, I can compute
expected viewing utility, and therefore predicted choice probabilities, for any new bundle (any
combination of the available channels). This allows me to predict bundle choices for various out-
of-sample unbundling counterfactuals.
The model does not contain any bundle-speci…c idiosyncratic shocks (e.g. an i.i.d. logit shock
for each bundle). This follows the pure characteristics model of Berry and Pakes (2007). This
feature of the model is important for unbundling counterfactuals, since they involve introduction
of a large number of new bundles in consumers’choice set. For example, if there are 50 channels,
under full a la carte consumers would be choosing among 250 possible combinations of channels on
cable. So, if the model contained an i.i.d. logit shock for each bundle (each possible combination of
channels), the distribution of the maximum of bundle utilities would be unreasonably high (since
at least some of the 250 i.i.d. logit shocks are likely to be extremely high), distorting the welfare
e¤ects and the predicted market shares. Berry and Pakes (2007) show that the pure characteristics
37 Notice that the “new products” introduced in counterfactuals are new combinations of existing channels, not new
channels.
38 Thus, if the local cable operator o¤ers a basic package, an expanded-basic package and HBO, the list of possible
cable bundles is: (1) basic, (2) basic + HBO, (3) basic + expanded-basic, (4) basic + expanded-basic + HBO.
39 Average, sum, weighted average, etc – whichever …ts the data the best. Notice that since EUiis the same for all
t-s (due to data limitations), I do not have to explicitly aggregate utility across periods.
13
model has more reasonable implications in counterfactuals that introduce a large number of new
alternatives.
Restrictive Assumptions and Possible Extensions
The main restrictive assumption in the model is that it treats all time periods as identical, i.e.
the systematic part of channel utilities is assumed to be the same for all t-s. I impose this assumption
because my data is not detailed enough to estimate a more ‡exible speci…cation. However, it can
be done with more detailed viewing data, for example viewing by day part for each channel. In
this case, I could estimate daytime and primetime utility for each channel, and I could allow the
e¤ect of viewing utility on bundle choices to vary by day part (for example, a higher weight on
primetime viewing utility than daytime).
Another potentially important factor is that viewers may value an hour of TV-viewing di¤er-
ently depending on the type of programming. For example, a hardcore sports fan may value watch-
ing the 2 hours a month that his team is playing on ESPN higher than the rest of his TV-viewing
combined (even if he spends much more time watching other channels). Such di¤erences would not
be fully captured by viewing utilities even with very detailed viewing data (notice that utilities in
the discrete-choice framework re‡ect the viewing choices but not the attention/involvement level
of these choices)40.
3.3. Identi…cation
The parameters of viewing utilities (j-s; ; ; )are identi…ed primarily by the viewing choices
in the data (conditional on bundle subscriptions). Individual-level data allows me to get reliable
estimates of the main parameters in channel preferences, because I directly observe choices of
multiple channels for each individual and household, and demographics for the same individual or
household. Thus, the covariances between viewing choices and demographics (which pin down )
and the covariances in viewing choices conditional on demographics (which pin down and )are
identi…ed directly from the data.
The identi…cation of j; ; and from the viewing choices is generally straightforward,
the only complication is that households self-select into di¤erent subscriptions depending on their
unobserved viewing preferences !h:Thus, the distribution of !hvaries by subscription, and in
particular the distribution of !hfor local-antenna households is di¤erent from that for cable/satellite
subscribers:For all those who subscribe to at least several cable channels, the distribution of !his
identi…ed from their viewing data for those channels. However, for local-antenna households (who
do not subscribe to any cable channels), the distribution of !his not directly identi…ed, because I do
not observe any valid cable viewership for them41. Besides identi…cation through functional form for
local-antenna households, part of the distribution of !hfor them is identi…ed by variation in cable
40 One way to proxy for such di¤erences could be to add reduced-form control for variables that may re‡ect viewers’
typical involvement or attention level for each channel (for example, channels’ advertising rates or license fees, after
controlling for their ratings and audience demographics).
41 In actual data, I observe some cable viewership by local-antenna households. However, it represents social
out-of-home viewership, which is not comparable to viewership by subscribers (primarily at home).
14
prices across locations, with prices a¤ecting subscriptions but having no direct e¤ect on viewing
utilities (similar to identi…cation through exclusion restrictions in Heckman selection model)42.
A secondary source of identi…cation for the parameters of viewing utilities is through variation
in channel lineups across locations and across tiers, and its e¤ect on bundle choices. For example,
if basic-only subscriptions are higher in areas where ESPN is carried on basic tier (as opposed to
expanded-basic), the model will attribute it to the viewing utility for ESPN.
The parameters in F(EU1; :::; E UKh);i.e. the e¤ect of expected viewing utility on bundle
choices, is identi…ed from several sources. One source is variation in demographics across house-
holds, which a¤ects both viewership and bundle choices. Thus, the co-movement between bundle
choices and viewership (driven by variation in demographics) will identify the e¤ect of viewing
utility on bundle choices.
Another important source of identi…cation for the parameters in F(EU1; :::; EUKh)is vari-
ation in cable packages across locations (illustrated in section 4.2). The e¤ect of channel lineups
on subscription choices, combined with the estimates of channel utilities from the viewing data,
will identify the parameters of F(EU1; :::; EUKh):Much of the variation in channel lineups is with
respect to the niche channels that relatively few people watch. However, among those who do watch
a given niche channel, the viewing time patterns are comparable to those for the major channels43 .
Thus, even though each niche channel has a relatively small total audience, its impact on subscrip-
tions among this audience can be comparable to the impact of the major channels. Furthermore,
di¤erent people like di¤erent niche channels, so the combined variation in niche channels might be
a¤ecting a large proportion of consumers. Also, the data has meaningful variation even for the
most popular channels such as CNN or ESPN. Although CNN and ESPN are available everywhere,
di¤erent cable systems place them on di¤erent tiers. For example, about 10% of systems carry
ESPN on the basic tier, and 90% on expanded-basic. If consumers value ESPN, the locations that
o¤er ESPN on basic tier will have a higher share of basic-only cable subscribers, at the expense of
other cable/satellite packages and local antenna. Also, some of the major channels exhibit much
more variation than ESPN (e.g. Discovery, Fox Sports and TBS –see section 4.2).
There is enough price variation across locations to identify the price sensitivity parameters.
Notice that despite the use of individual-level data, price endogeneity is still a concern (as discussed
in Berry, Levinsohn, Pakes [2004]). Although the channel lineup of a cable package (which I control
for) fully summarizes the physical product characteristics, important unobserved determinants of
demand may include the quality of customer service and marketing e¤ort. Both likely vary across
cable systems, and both are likely correlated with price. This gives rise to price endogeneity, which
can be dealt with using standard methods44.
42 For example, compare two locations with similar cable packages, but lower prices in one location. Some of the
households who would have chosen local antenna in the higher-priced location subscribe to cable in the lower-priced
location, thus the distribution of !hamong cable subscribers di¤ers across the two locations. As a result, the
distribution of observed viewing choices (among cable subscribers) will also di¤er across the locations.
43 For example, just 4% of consumers watched the Independent Film Channel (IFC) last week, vs 28% for Discovery.
However, an average IFC viewer spent 2.7 hours watching IFC, vs 2.6 hours for an average Discovery viewer.
44 Another related concern is possible endogeneity of channel lineups. For example, cable operators may o¤er a
more attractive channel lineup in markets with higher (or lower) marketing e¤ort. Most empirical literature in IO
15
4. Data
I use data from several sources. Simmons National Consumer Survey (May 2003 – May 2004)
provides individual-level data on cable/satellite subscriptions and viewership for 64 main cable
channels. The Television and Cable Factbook (2005) provides characteristics of cable packages for
each location. The license fees data is from SNL Kagan’s Cable Program Investor, May 2007.
In the empirical analysis, I focus on 4 metropolitan areas: New York, Los Angeles, San
Francisco and Boston45. All the descriptive statistics in this section refer to these 4 areas.
4.1. Individual-Level Cable Viewership Data (Simmons National Consumer Survey)
The Simmons National Consumer Survey data is based on a self-administered paper survey con-
ducted between May 2003 - May 2004. For each household, it samples all household members above
age 18.
For each household in the sample, I observe household demographics and some information
on their cable/satellite subscriptions and location. Subscriptions data consists of binary variables
for: (1) analog cable (but not basic vs expanded-basic), (2) digital cable, (3) satellite (but not
speci…c satellite package or provider), and (4) subscriptions to the main premium channels (HBO,
Cinemax, Encore, The Movie Channel, Showtime and StarZ).
For household location, I observe state and DMA code46 . Notice that each state and DMA
contain multiple cable systems, with substantial variation in cable packages and prices, so the
location variables are not detailed enough to identify the exact menu of packages and prices facing
each household. In the empirical analysis, I solve this problem by integrating out household’s
unobserved location within the DMA (section 5.3).
For each individual, I observe demographics and cable viewership data. The viewership data
records how much time the individual spent watching each of the 64 main cable channels in the
past 7 days (a 64 1vector of viewing times for each individual)47. This data covers most of the
cable channels typically carried on basic and expanded-basic tiers (the main exception is regional
sports networks like NESN or YES), and many of the digital tier channels. The viewership data
is self-reported by the respondent at the end of the week. This reduces the accuracy of the data.
On the other hand, a useful advantage of self-reported data (compared to automatically-recorded
assumes that all product characteristics except price are exogenous, and the justi…cation is that they are much harder
to change than price. Notice that the same is true for channel lineups, since cable operators are locked in multi-year
contacts which usually stipulate a speci…c tier for each network.
45 I drop the rest of the data due to very time-consuming data entry (the Factbook data I have is on paper, and
each large metropolitan area contains dozens of cable systems, with a lot of data for each system). Notice that cable
operators set the prices and part of channel lineups of their packages locally, so I do not need a nationwide sample
to have meaningful counterfactuals.
46 A DMA (Designated Market Area) is a broadcast TV market as de…ned by Nielsen Research. For the largest
DMAs, the DMA boundaries are roughly similar to the corresponding metropolitan area (for example, Boston DMA
covers most of Eastern Massachusetts and parts of Vermont and New Hampshire). I observe DMA codes only for 14
largest DMAs.
47 This does not include viewership for broadcast networks (ABC, CBS, NBC, FOX, etc). The dataset contains
some data for broadcast networks, but the variable de…nitions are quite di¤erent, and cannot be easily combined with
the cable viewership data in estimation.
16
Nielsen data) is that the respondent is likely to remember and report the occasions when she was
actually watching TV (i.e. paying some attention), as opposed to TV just being on (which would
count as viewership in Nielsen data).
One important issue is missing data. First, although Simmons attempts to sample all house-
hold members above age 18, many households (about 33%) have missing household members in
the data. In addition, about 5% of respondents did not …ll out the cable viewership part of the
questionnaire at all, or reported unreasonable numbers (such as total viewing time above 24 hours
a day). In the empirical analysis, I drop households with missing household members, since cable
subscription is a household-level decision48. In addition, if a respondent did not …ll out the TV-
viewing part of the questionnaire or reported unreasonable total viewing time (de…ned as above
70 hours a week), I treat her viewing choices as unobserved in the TV-viewing part of the model
(but I keep the household in the bundle-choice part of the model). If these data problems are
independent of consumers’unobserved viewing preferences, this sample selection does not bias the
estimates. Also, I drop households that do not own a TV (about 2%). The …nal sample in the
empirical analysis is 2314 households, containing 4846 individuals above age 18 (95% of them with
valid viewing data).
Table 1 summarizes subscription choices in the data. Compared to the national data from
FCC (2005a), the proportion of local-antenna households in the 4 DMAs is somewhat higher (23% vs
16%), and the proportion of cable subscribers is somewhat lower (57% vs 65%). This is reasonable,
since the quality of the outside alternative (not watching TV) is likely to be higher in the 4 large
metropolitan areas I focus on.
Table 2 summarizes viewership for each channel, among cable and satellite subscribers. Notice
that the di¤erences in viewership across channels re‡ect not only di¤erences in channel utilities, but
also di¤erences in channel availability and tier placement (both are accounted for in the empirical
model). The most popular channels are CNN, Discovery, HBO, TBS and TNT, with a weekly
audience of 24-28% of all cable and satellite subscribers. Interestingly, even though there are
dramatic di¤erences in the audience size across channels, from 0.7% for Fuse to 28% for Discovery,
the average viewing time (among the audience of a given channel) is comparable, e.g. 2.3 hours
a week for Fuse vs 2.6 hours for Discovery. Thus, even though niche channels appeal to much
fewer consumers than major channels, the intensity of preferences (among their target audience) is
comparable.
Table 3 presents correlations in viewing times across channels, for several major representative
channels. The correlation patterns are quite intuitive, for example the highest correlations (for the
column channel in the table) are between Cartoon Network and Nickelodeon; CNN and CNN
Headlines/Fox News; Discovery and History Channel; ESPN and ESPN2/FOX Sports; MTV and
VH1. When I compute correlations between each pair of channels in the data, most of them are
close to zero (93% are below 0.2, and two thirds are below 0.1), suggesting large potential for
discriminatory e¤ects of bundling.
48 Besides missing household members above age 18, in all cases I do not have detailed data for children, who may
also in‡uence subscriptions. I tried adding reduced-form control for children in household’s bundle choices, but it
was insigni…cant in all cases.
17
There is a lot of cable viewing by non-subscribers. For example, 39% of local-antenna respon-
dents (who do not subscribe to any cable channels) report watching some cable channels last week,
and on average they watched about 11 hours of cable last week (among those who report non-zero
time). Furthermore, they watch a wide variety of channels, i.e. it is not just sports channels in
bars49 . In the empirical model, I explicitly account for viewership by non-subscribers.
4.2. Characteristics of Cable Packages (The Television and Cable Factbook)
The Television and Cable Factbook is the standard source of data for the cable industry. It provides
detailed characteristics of cable packages for each cable system50 in the US. For each system I
observe: (1) locations served by the system, (2) channel lineup and price for each package, and (3)
prices for premium channels. I use the 2005 edition, which contains data for 2004.
The Factbook data su¤ers from two main problems. One is missing data for prices51 . Another
is non-updating of data. For example, when I compare the 2004 and 2005 editions of the Factbook,
the data for most of Adelphia, Cablevision and Charter systems is identical for both years, which
suggests that the data was not updated in the 2005 edition. I.e., the data for them is for 2003
or earlier, however it does not appear to be heavily out-of-date (their channel lineups and prices
are quite similar to those for systems with up-to-date data). On the other hand, the data for all
Comcast, Cox and Time Warner systems (a majority of all cable systems in my sample) was updated
in the 2005 edition, i.e. the data for them is for 2004. Despite these problems, the Television and
Cable Factbook is the standard source of data, both for industry practitioners and for academic
research (e.g. Goolsbee and Petrin [2004], Crawford [2008], Chipty [2001]).
In the empirical analysis, I focus on cable systems in four metropolitan areas (DMAs): Boston
(MA only), Los Angeles, New York (NY state only) and San Francisco. I drop “overbuild”systems
and systems with less than 2000 subscribers52 . My …nal sample is 140 systems, serving about 8
million cable subscribers (12% of US total). The cable systems range in size from a few thousand to
1.4 million subscribers (Time Warner Cable in Manhattan), with a median system serving around
30,000.
There is a lot of variation across cable systems in prices and channel lineups (for the same tier).
Figure 1 illustrates variation in prices and number of channels for the most popular combination
of packages (basic and expanded-basic combined). The price ranges from $11.50 to $63.80, and the
49 A recent study by Arbitron also …nds that 35% of respondents watch TV outside their home each week (mostly
at a friend’s house, bars/restaurants or at work), and their viewership is spread over various genres (Media Life
Magazine, Apr 9, 2007).
50 A “cable system” is de…ned as a community or several communities that are o¤ered the same services at the
same prices from the same cable company (i.e., it is not necessarily one physical network). Large cable operators
often own dozens of systems within the same metropolitan area.
51 Prices are missing for 7% of basic packages, 11% of expanded-basic packages, and 25% of digital packages in my
sample. In the empirical analysis, I …ll missing prices using a regression of package prices on package characteristics,
separately for each tier.
52 “Overbuilders” (recent cable entrants competing with the incumbent) are present in a very small number of
locations (e.g. some parts of Manhattan). I drop them because I do not observe speci…c neighborhoods in which
they are active, and their market share is negligible. I drop the systems with less than 2000 subscribers because their
share of all subscribers is negligible, while the data entry time is the same as for other (much larger) systems.
18
number of cable channels ranges from 13 to 71 (this does not include broadcast channels). Some of
this variation is variation across di¤erent cable operators and across di¤erent metropolitan areas,
however the variation in prices and channel lineups is substantial even when I focus on the systems
owned by the same cable operator within the same metropolitan area. Possible reasons for such
variation are discussed later in this section.
Variation in Channel Availability
An important source of identi…cation is variation across cable systems with respect to channel
availability and tier placement. Table 2 presents availability and tier placement numbers for each
channel (all the numbers are weighted by the number of subscribers for each system). Availability
of most niche channels varies across cable systems. However, major cable channels are available
essentially everywhere. For them, the main source of variation is with respect to their placement
on a speci…c tier (basic, expanded-basic, or digital-basic). For some channels with total availability
close to 100%, there is a lot of variation with respect to their placement on digital vs analog tiers.
For example, the break-down between digital and analog tiers is 15% vs 81% for the Sci-Fi channel,
8% vs 87% for FOX Sports, and 11% vs 83% for Disney Channel (their total availability, on any
tier, is the sum of the two numbers).
However, the most popular channels (e.g. CNN, ESPN, USA) are never placed on the digital
tier. For them, the variation is with respect to their placement on basic vs expanded-basic tier.
For example, the break-down between basic and expanded-basic for ESPN is 10% vs 90%, for
CNN 5% vs 95%, and for USA 7% vs 93%53 . Thus, there is some meaningful variation even for
the most popular channels. Furthermore, the 5-10% of systems that carry CNN or other major
channels on the basic tier do not appear unusual in terms of area demographics. Also, there is
much more variation for some of the major channels, for example, the break-down between basic
and expanded-basic tiers for TBS is 45% vs 53%, and for Discovery it is 24% vs 73%.
What is driving the variation in cable packages?
Several factors can explain the variation across cable systems. First, the price of basic
cable is usually regulated by the local authorities, which may a¤ect the optimal allocation of
cable channels between basic cable and other packages, and the optimal prices of other packages.
Second, some cable channels are vertically integrated with cable operators, which a¤ects their
choice of which channels to carry. Chipty (2001) …nds that vertically-integrated cable operators
are more likely to carry the channels they own, and less likely to carry competitors’channels. Also,
the terms of carriage agreements with the cable channels (including wholesale bundling and tier
placement requirements) likely di¤er across cable operators, depending on their bargaining power
and when they last renegotiated the contract54. Third, even for the same cable operator in the same
metropolitan area, di¤erent locations have di¤erent age and quality of cable infrastructure, which
53 This break-down refers only to the systems that o¤er separate basic and expanded-basic packages (a small
percentage of systems merge them into a single “basic” package).
54 Also, it appears that the carriage agreements for the broadcast networks are often negotiated with their local
a¢ liates, separately for each broadcast market, and the terms of these agreements (including the wholesale bundling
requirements for cable channels a¢ liated with the broadcast network) vary across markets.
19
a¤ects the optimal con…guration of cable packages55. Finally, the distribution of demographics
di¤ers across locations, so it is probably optimal for cable operators to o¤er di¤erent channel
lineups and prices in di¤erent locations.
5. Empirical Speci…cation
The empirical speci…cation follows the general structure of the basic model (section 3), with some
modi…cations and additional details to accurately capture the practical details of cable subscriptions
and viewership.
For each individual i= 1:::Khwithin household h, I observe demographics Xh;i and a vector
of viewing times (Th;i;1; :::; Th;i;64)for the 64 main cable channels, where Th;i;j denotes the total
time spent watching channel jin the past 7 days. For each household, I also observe its cable or
satellite subscription. For each location, I observe the characteristics of all available cable packages
(the characteristics of satellite packages are the same everywhere in the US).
The model is presented backwards. First, I present the TV-viewing part of the model (stage
2), conditional on household’s subscription to a speci…c TV bundle (bundle of channels). Then, I
present the bundle choice part of the model (stage 1).
5.1. TV-viewing Conditional on the TV Bundle (Stage 2)
I model the viewing choices for each individual i= 1:::Khwithin household h. The household
subscribes to a TV bundle (Sh; Nh), where Shis the list of “core”cable channels (channels covered
in the viewing data), and Nhis the number of “other”cable channels (channels not covered in the
viewing data, mostly niche channels with much lower viewership). Notice that (Sh,Nh)only refers
to cable channels, i.e. it does not include broadcast networks56. For local-antenna households,
Sh=?and Nh= 0:
There are 7days, 20 half-hour periods each day. In each period t, individual ichooses one
of the “core”cable channels (j= 1:::64) or the outside alternative (j= 0), which pools together
watching one of the Nh“other”cable channels, watching a broadcast channel, or not watching TV.
Utility for the “core”channels. Individual ihas observed demographics Xh;i and unobserved
preferences wh;i. In each period, her utility from watching channel j= 1:::64 is
Uh;i;j;t =fj(j+Zjh;i +h;i;j )Ifj =2Shg j+"h;i;j;t (5.1)
where jis a vertical characteristic of channel j,"h;i;j;t is an i.i.d. logit (type-I extreme value)
shock, and the rest of the terms are discussed below.
55 For example, the capacity of Comcast’s system in Boston, MA, is 104 standard 6MHz channels, vs 77 channels in
nearby Cambridge, MA. Besides historical factors and cable companies’gradual infrastructure upgrade schedules, the
age and quality of physical infrastructure may be in‡uenced by the local authorities. For example, in franchise renewal
negotiations with Comcast in 2002, Boston mayor imposed speci…c deadlines and conditions on its infrastructure
investment in the city (http://www.cityofboston.gov/cable/franchise.asp).
56 Although there might be some variation in the availability of “fringe” broadcast channels, the major broadcast
channels are available everywhere, and cable operators are required to carry broadcast channels on the basic tier.
20
The term Zjh;i captures the utility from observed (to the researcher) horizontal channel
characteristics Zj:The preferences for Zjare h;i =Xh;i +w
h;i;where Xh;i are individual i’s
observed demographics;and w
h;i are her unobserved preferences. To account for possible within-
household correlations, I specify w
h;i as w
h;i =ew
h+ew
h;i;where ew
h;i sN(0;);ew
hsN(0; )57 .
Matrices ,and scalar are free parameters in estimation. Channel characteristics Zjinclude
channel genre and target demographics58.
The next term, h;i;j ;represents channel random e¤ects, with a ‡exible covariance structure
across channels. It captures unobserved channel preferences (beyond the patterns captured by
unobserved preferences for Zj). In principle, one could estimate a ‡exible 6464 covariance matrix
of h;i;j for the 64 channels in the data, however that would require estimating an unreasonably large
number of parameters. To reduce the number of parameters, I use a factor-analytic speci…cation
h;i;j = jw
h;i
where jis a length-Mvector of free parameters for channel j, and w
h;i are the Mcommon
factors. I specify w
h;i as w
h;i =ew
h+ew
h;i;where ew
h;i sN(0; IM),ew
hsN(0; IM);and re‡ects
correlations across household members. The j-s capture the covariance structure of unobserved
channel preferences h;i;j. For example, two channels with similar j-s will have strongly positively-
correlated h;i;j -s, while two channels whose j-s are orthogonal to each other will have uncorrelated
h;i;j -s. Factor-analytic speci…cations are standard in marketing (e.g. Elrod and Keane [1995]),
but less common in economics (exceptions are Sargent and Sims [1977], Stock and Watson [1999,
2002]). While the main objective in these papers is to identify a small number of meaningful
common factors, my objective is more modest: to …nd a parsimonious reduced-form speci…cation
that approximates the covariances across channels reasonably well. In estimation, I choose the
number of factors Mhigh enough to capture the covariances in the data reasonably well, but low
enough to keep the number of parameters manageable59 .
The non-linear transformation fj(j+Zjh;i +h;i;j )allows me to …t the main patterns in the
data much better than a more standard linear speci…cation. Speci…cally, when I estimated various
linear speci…cations (with normally-distributed unobserved preferences), the model successfully
captured the mean time spent watching each channel, but heavily overpredicted the proportion of
non-zero viewing times (i.e. in the data, say 10% watch channel jfor 3hours, while the model would
57 The only reason I restrict the covariance matrices of ew
h;i and ew
hto be proportional to each other is to reduce
the number of parameters in estimation (the data allows to identify more ‡exible speci…cations).
58 I classify all channels into 7 mutually-exclusive genres (see table 2 for details). I compute “target demographics”
for each channel as the average demographics of its national audience (% males, age, % blacks, % college grads and
% households with children). To reduce the number of parameters in estimation, Zjh;i for each of the “target
demogr aphics” variables in Zjonly includes interaction with the corresponding respondent demographic (age enters
as a squared di¤erence), without UH. Also, I control for the di¤erence between DMA rating and national rating for
each channel, to proxy for regional di¤erences in preferences.
59 One way to think about the identi…cation of Mand j-s is as follows. First, estimate the model with a ‡exible
64 64 covariance matrix for h;i;j -s. After that, try to approximate with a much smaller number of parameters
;increasing the number of dimensions (i.e. the number of free parameters in ) until the …t is good enough:To
reduce the number of parameters, I estimate jonly for the 32 most popular channels (which account for about 75%
of total viewership), and set it to zero for the rest of the channels.
21
predict 30% watching it for 1hour). In other words, the model would overpredict the proportion
of consumers with moderately low preferences for channel j(occasional viewers), at the expense of
very low and high preferences. The reason is that normal distribution has the highest density at
moderate preferences. In contrast, the patterns in the data are consistent with a certain fraction
of consumers having high preferences for channel j(heavy viewers), a large majority having very
low preferences (non-viewers), and relatively few consumers having moderately low preferences
(occasional viewers). One natural solution could be to use a di¤erent distribution for unobserved
preferences (e.g. lognormal or exponential). However, I have to model covariances across 64
channels, across di¤erent product characteristics and across di¤erent household members, and that
would be more di¢ cult to do with a di¤erent distribution (for example, while the sum of two
normal distributions is also normal, and its covariance structure is easy to characterize, this is not
the case for the sum of two lognormal or exponential distributions). Therefore, a more transparent
approach is to build up the covariance structure of preferences using normal distributions, and then
to transform the resulting distribution of channel utilities j+Zjh;i +h;i;j using fj(:::):I specify
fj(:::)as
fj(u) = u0
1
1 + e1(uf
j)
where 0; 1and f
1:::f
Jare free parameters. It nests the linear speci…cation for 0= 0:If 1is
large relative to the range of u=j+Zjh;i +h;i;j;then fj(u)ufor uabove the threshold f
1;
but drops to fj(u)u0below the threshold. This allows me to match the proportion of heavy
viewers and non-viewers without overpredicting the number of occasional viewers.
The term Ifj =2Shg japplies only if household’s subscription does not include channel j.
In the data, I observe a lot of cable viewing by non-subscribers, so I cannot just exclude channel
jfrom their choice set. I assume that in such cases channel jis still in consumer’s choice set, but
she has to incur disutility j= 0+ ZZjto get access to it elsewhere (at a friend’s house, in a
bar, etc), where the -s are free parameters in estimation60.
Outside alternative. The outside alternative pools together several options: watching one of
the Nh“other” cable channels that the household subscribes to, watching broadcast TV or not
watching TV. Notice that I do not have viewing data for the “other” cable channels and for the
broadcast channels.
I specify the utility from each of the “other” cable channels k= 1:::Nhas other +"other
h;i;k;t,
where "other
h;i;k;t is an i.i.d. logit shock. The utility from watching broadcast TV or not watching TV at
all is normalized to 0+ "out
h;i;t. Notice that this is a normalization (as opposed to a restriction), which
is standard for discrete-choice models: the constant term and the coe¢ cients on demographics have
to be …xed for one of the alternatives.61
The combined outside utility in each period is the maximum of "out
h;i;t and other +"other
h;i;k;t for
60 In preliminary estimation, I also allowed jto depend on consumer’s demographics, however the coe¢ cients on
demographics were insigni…cant.
61 Also, all TV bundles (including local antenna) include the same major broadcast channels, so I do not have to
control for di¤erences in broadcast channels across bundles.
22
k= 1:::Nh. For logit "-s, it is equivalent to
Uh;i;0;t = ln (1 + Nhexp(other))+"h;i;0;t
where "h;i;0;t is an i.i.d. logit shock (Johnson, Kotz, and Balakrishnan [1995]).
Two additional issues might be important in actual TV-viewing behavior. One is switching
costs and/or variety-seeking, which would introduce dependence in channel choices across periods.
Another is joint TV-viewing by multiple household members, which would introduce dependence in
choices across individuals. Due to data limitations, I do not explicitly model such dependences, and
they are proxied (in reduced form) via correlations in unobserved heterogeneity, across channels
and across household members.
Expected utility from TV-viewing for bundle Sh; Nh
Households’ bundle choices are driven by the utility they expect to get from watching the
channels in the bundle. The information and timing assumptions are the same as in the basic model
(section 3.1).
The expected utility from bundle Sh; Nhat the subscription stage is
EU (Sh; NhjXh;i; wh;i )EmaxfUh;i;j;t gj2fSh;0gjXh;i; wh;i
For logit "h;i;j;t-s, it has an analytical expression (Ben-Akiva [1973])
EU (Sh; NhjXh;i; wh;i ) = ln 0
@X
j2fSh;0g
exp(Uh;i;j;t)1
A(5.2)
where
Uh;i;j;t Uh;i;j;t "h;i;j;t =(fj(j+Zjh;i +h;i;j )for j2Sh
ln (1 + Nhexp(other )) for j= 0
In computing this expected utility, I restrict attention to the channels that are included in
the bundle (j2 fSh;0g). Notice that the viewership model above allows consumers to also watch
channels they do not subscribe to, after incurring disutility jto get access to them elsewhere.
A plausible alternative could be to compute expected utility from all TV-viewing from all sources
(with disutility jsubtracted for j =2Sh):I chose to focus only on the channels in the bundle for
several reasons. First, the viewership by non-subscribers is mostly social viewership, thus jalso
captures the utility from its social aspects. Second, by focusing only on the channels consumers get
in their subscription, it probably provides a more reasonable description of their actual subscription
behavior. Finally, in preliminary estimation for a speci…cation that nests both possibilities, the
estimates supported focusing only on the channels in the bundle.
5.2. Bundle Subscription Choice (Stage 1)
23
At the subscription stage, household hchooses from the list of available TV bundles. One possibility
is to use the local antenna, which is free but only o¤ers local broadcast channels (i.e., Sh=?;
Nh= 0). Another possibility is to subscribe to various combinations of base packages and premium
channels on cable or satellite.
A fully structural approach would be to compile the full list of all possible TV bundles for each
household (all possible combinations of base packages and premium channels on cable and satellite),
and then to compute expected viewing utilities and choice probabilities for each of the bundles on
this list. However, the full list of all possible TV bundles contains hundreds of combinations62,
which would slow down the estimation too much.
To reduce the number of combinations in estimation, I make several simplifying assumptions.
First, I separate between the choice of base packages and the choice of premium channels. Specif-
ically, I assume that consumers …rst choose the base packages, ignoring the e¤ect of subsequent
premium subscriptions on bundle utility63. After that, they choose the premium channels. Second,
instead of including in the choice set all possible satellite packages from DirecTV and Dish, I restrict
attention to the most popular satellite package, DirecTV Total Choice64.
Choice of the “base bundle” (combination of base packages)
Household hlives in a location served by cable system m. Her choice kis among the fol-
lowing mutually-exclusive combinations of base packages: (1) local antenna, (2) basic, (3) basic
+ expanded-basic, (4) basic + expanded-basic + digital-basic, and (5) DirecTV Total Choice on
satellite.
Each alternative k= 1:::5is characterized by the list of the “core”channels Sm;k, the number
of “other” channels Nm;k , and price Pm;k .65 The characteristics of cable alternatives (2)-(4) vary
across cable systems m, while (1) and (5) are the same nationwide. The subscription-stage utility
for alternative kis speci…ed as
Vh;k =F(EU1; :::; E UKh) + (Xh; wp
h)Pm;k +kWh+m;k +h;f(k)(5.3)
where EUiEU (Sm;k; Nm;kjXh;i; wh;i )is the expected viewing utility for bundle kfor each house-
hold member i= 1...Kh;the function F(:::)aggregates household members’ preferences, and
(Xh; wp
h)is the price coe¢ cient that depends on household demographics Xhand unobservable
wp
h:These parts of bundle utility are similar to the basic model (section 3.2), while the new terms
62 Speci…cally, there are 26= 64 possible combinations of the premium channels in the data, and each of them can
be matched with 3possible combinations of base packages for a typical cable system, for a total of 192 possible cable
bundles (and a comparable number of satellite bundles).
63 This simpli…cation can a¤ect the results, for example if the relative ranking of the base packages plus HBO is
di¤erent from the ranking of the same packages without HBO.
64 Besides reducing the computational burden, another reason for using this assumption is that I do not observe
speci…c provider and package for satellite subscribers in my data. An alternative could be to model choices for each
satellite package separately, using the total for all satellite packages in estimation.
65 In terms of notation, Sh; Nhin the previous subsection denotes the characteristics of the bundle chosen by
household h, while Sm;k ; Nm;k ,Pm;k in this subsection denote the characteristics of the k-th bundle available in the
locations served by cable system m.
24
kWh+m;k +h;f(k)are discussed later. I specify F(:::)as
F(EU1; :::; E UKh)(0+1Kh)1
KhX
i=1:::Kh
EUi
where the coe¢ cients 0; 1allow for some ‡exibility with respect to how household members’
utilities are aggregated66. I specify the price coe¢ cient as
(Xh; wp
h)0+Inc Inch+wwp
h
where Inchis household income and the price-sensitivity unobservable wp
hsN(0;1)67:
The new terms introduced in the empirical speci…cation are kWh+m;k +h;f(k): Whdenotes
household characteristics that can a¤ect bundle choices directly (i.e. not through the viewing
utility)68. The vertical characteristic m;k captures several things. For cable companies, many
decisions are made at the level of individual cable systems m, so m;k for cable (k= 2:::4) likely
varies across m(re‡ecting di¤erences in customer service and marketing e¤ort across systems), and
also across bundles (re‡ecting the emphasis they place on di¤erent packages in their marketing and
sales). For local antenna (k= 1) and satellite (k= 5), I assume that m;1=1and m;5=5
everywhere, re‡ecting the average attractiveness of these options. For example, 1can capture the
lower picture quality for local antenna, while 5can re‡ect the generally superior customer service
for satellite (compared to cable) but also more complicated installation69 .
Unlike in the basic model, in the empirical speci…cation I also include idiosyncratic shocks
h;f(k);where f(k)refers to the provider of the bundle (f(k) = 1 for local antenna, f(k) = 2 for
any cable bundle k= 2:::4, and f(k) = 3 for the satellite bundle). The h;f -s are i.i.d. logit shocks,
capturing idiosyncratic preferences for speci…c providers (but the same draw of h;f applies to all
the cable bundles). This is a hybrid speci…cation following Song (2008), halfway between a pure-
characteristics speci…cation and random-coe¢ cients logit. I use this speci…cation because it …ts the
data better than a pure-characteristics speci…cation (Berry and Pakes [2007]), and there are good
a priori reasons to expect idiosyncratic shocks in preferences for di¤erent providers70. (Another
66 Speci…cally, if 0= 0; 1>0;then the subscription choices are driven by the sum of household members’utilities,
if 0>0; 1= 0;then they are driven by the average, and if 0>0; 1>0;then it is something in between. I
also tried estimating di¤erent weights for di¤erent household members depending on their demographics, however
the coe¢ cients on demographics were insigni…cant.
67 Normal distribution implies that a certain percentage of consumers have a positive price coe¢ cient, however
this percentage in the …nal estimates is quite low (I truncate their price coe¢ cient at a slightly negative value in
counterfactuals, since otherwise the optimal price is +1):I tried using log-normal and truncated normal distributions
in estimation, however the resulting distribution looks very close to normal (also, notice that the price coe¢ cient for
the least price-sensitive consumers is not identi…ed from the data anyway).
68 Whonly applies to satellite, and captures physical factors that a¤ect satellite availability. Speci…cally, Whincludes
DMA dummies and dummies for apartment building and rented house/apartment.
69 Notice that satellite prices and packages are the same nationwide, re‡ecting a much more centralized structure.
Thus, we are unlikely to have signi…cant within-DMA variation in customer service and marketing e¤ort for satellite
providers (notice that kWhcaptures the variation across DMAs).
70 For example, the quality of local-antenna reception varies across households for idiosyncratic physical reasons.
Likewise, in the same apartment building, an apartment facing south might have ideal physical conditions for satellite
reception, while a next-door apartment facing north might have no clear line of sight to the satellite.
25
possibility could be to use standard random-coe¢ cients logit, which is more common than pure-
characteristics speci…cations. However, as discussed in section 3.2, it would give unreasonable
predictions in unbundling counterfactuals).
Choice of the premium channels
After choosing the base packages, household hchooses premium channels. Consumers can
subscribe to any combination of available premium channels, with fees around $10-15 per channel.
I use a reduced-form speci…cation. Household hsubscribes to premium channel j=fHBO;
Cinemax; Encore; Showtime; StarZ; T he M ovie Channelg71 with probability
Pprem
h;j =g(prem
j+ (prem
0+prem
1Kh)Uh;j +prem
Inc I nch)(5.4)
where g(z)exp(z)=(1 + exp(z)),Khis the household size, Uh;j is the average of household
members’viewing utilities for channel j(excluding the logit shocks), and Inchis household income.
Notice that the choices are not mutually-exclusive across channels.
I do not control for premium prices because of data quality issues in the Factbook. They are
more severe for premium channels than for the base packages72, and as a result measurement error
accounts for a large fraction of overall variation in premium prices.
5.3. Integrating out Unobserved Locations within the DMA
As mentioned in section 4.1, I do not observe household’s exact location within the DMA. Thus,
the cable system mthat enters each household’s choice set is unobservable to the researcher. In
estimation, I use the standard solution of integrating out the unobservables (the cable system
serving each household). I do it in several steps.
First, I use the 5 percent Census microdata (2000) to estimate the coe¢ cients on household
demographics for each location. The most detailed location variable in Census microdata is a
PUMA (public-use microdata area), an area with population of around 100,000 people73 (a city,
part of a city, or several smaller communities combined). For each DMA, I estimate a multinomial
logit of household’s PUMA on household demographics.
Next, I use these estimates to compute the predicted distribution of PUMAs for each house-
hold in the Simmons data. After that, I link PUMAs to the service areas of di¤erent cable systems,
in order to obtain the probability distribution of cable systems for each household, Pr(mhjXh),
conditional on household demographics Xh(mindexes all the cable systems within the DMA).
71 All of them are available on satellite. For cable, availability varies across cable systems, so I restrict the set of
channels if necessary. Households that chose local antenna cannot subscribe to any premium channels.
72 One issue is a high proportion of missing or dubious prices. Also, some operators combine two premium channels
into a single mini-package (e.g. StarZ + Encore), which is often not rep orted in the data. Finally, on many (but
not all) cable systems consumers have to rent a set-top box for an additional fee in order to be able to get premium
channels, and I do not observe which systems require that.
73 Summary statistics from the Census are available at a much …ner level of locations. However, this is not the case
for publicly-available Census microdata, due to privacy concerns.
26
6. Estimation
I estimate the model by simulated GMM. All the moments are simulated in an unbiased way for
each household, and the number of households is large, therefore the estimates are consistent for a
small number of simulation draws.
For each household h, I observe its cable/satellite subscription, Baseh=kfor the base
bundles (k= 1 for antenna, k= 2:::4for the cable bundles, k= 5 for satellite), and P r emh;a
vector of six binary variables for premium subscriptions. For each individual i= 1:::Khwithin
household h, I observe demographics Xh;i and a vector of viewing times (Th;i;1; :::; Th;i;64)for the
64 main cable channels, where Th;i;j denotes the total time spent watching channel jin the past 7
days. Also, for each household I have the estimated distribution of cable systems within the DMA,
Pr(mhjXh);conditional on household demographics Xh(section 5.3).
6.1. Price Endogeneity
The unobserved vertical characteristics for cable bundles, m;k;are likely correlated with the price
Pm;k. Without proper control for m;k-s;the price coe¢ cient is likely to be upward biased (e.g.
Berry, Levinsohn, Pakes [1995]).
One standard solution for price endogeneity, BLP (Berry, Levinsohn, Pakes [1995]), is based
on inverting the market shares in order to back out the m;k-s. However, this approach requires a
large number of simulation draws in estimation (since the imputed -s are not linear with respect to
the simulation error), with multiple evaluations of predicted market shares within the contraction
mapping routine. This makes it computationally impractical in my case. The second standard
solution in the literature, micro-BLP (Berry, Levinsohn, Pakes [2004]), is based on estimating the
vertical constants m;k for each good using micro-data, and then doing an IV regression of m;k -s
on product characteristics and price. However, I do not have enough observations per cable system
to reliably identify the m;k-s for each of the 140 cable systems in the data, so this approach is also
not practical in my case74.
The most practical alternative is the control function approach of Petrin and Train (2006).
Although there is some controversy around this approach (in particular, Wolak [2003]), Petrin and
Train report that their control-function estimates of price elasticity for cable television (and several
other examples) are very close to BLP-style estimates. This approach is based on inverting the
equilibrium pricing function to control for the unobservables that give rise to price endogeneity.
Speci…cally, the vector of equilibrium prices in market mcan be written as Pm=p(Sm; m);where
p(:::)is the equilibrium pricing function; Smis the vector of demand and supply shifters for all
the products in this market, and mis the vector of product-speci…c unobservables. Under certain
regularity conditions, this price function can be inverted, allowing to recover a one-to-one function
74 I have experimented with more restrictive speci…cations that can be identi…ed from my data. Speci…cally, I
allowed m;k -s to vary across tiers, DMAs and cable companies (but not across individual cable systems), and allowed
them to depend on the observed characteristics of the system. However, this did not reduce the upward bias in
the price coe¢ cient, suggesting that price endogeneity is mostly due to unobserved variation across individual cable
systems.
27
of ; denoted by e
: Then, after controlling for e
in the demand model, price is no longer endogenous.
Following Petrin and Train (2006), I use a simple version of the control-function correction.
Speci…cally, …rst I run OLS regressions of bundle price on demand and supply shifters, separately
for basic, expanded-basic and digital-basic cable. The explanatory variables I use are average
demographics in the area served by cable system m, its channel capacity and availability of phone
services (a proxy for technology), DMA dummies, dummies for the 5 largest cable companies, the
number of cable channels on each tier and the total cost of license fees for each tier75. The price
residuals vector e
m= (e
m;basic;e
m;exp-basic;e
m;digital)is approximately a one-to-one function of the
m;k-s. After that, I estimate the full structural model with m;k for cable bundles speci…ed as
m;k =k+ke
m;where kand kare free parameters in estimation76.
6.2. The Moments
In estimation I match several groups of moments. The details of simulation and computation are
in appendix A.
The …rst group of moments is viewing-time moments. For each of the 64 channels, I match
actual and predicted average viewing time, and predicted and actual proportion of non-zero time.
This pins down the vertical constants j; f
jfor each channel77:Also, I match the covariances be-
tween viewing choices and demographics multiplied by observed channel characteristics (summed
over all channels to reduce the number of moments). This pins down the coe¢ cients on demo-
graphics in viewing preferences:To identify 0; Z, I match the covariance between viewing time
for channel jand binary variables for whether or not it is available on basic, expanded-basic and
digital cable in household’s location, and covariances with the same binary variables multiplied by
channel characteristics (summed over all channels to reduce the number of moments).
For each channel j, I also match actual and predicted covariances between the viewing time
for channel jand the rest of the channels combined. For each pair of channels j; k (among the
top-32 channels for which I estimate the factor-analytic term j!
h;i), I match actual and predicted
covariances between the viewing times for those two channels78. These covariances pin down the
unobserved heterogeneity parameters and j:To identify the within-household correlation pa-
rameter ; I also match predicted and actual covariances of total viewing time between di¤erent
household members.
75 Since the number of observations in these regressions is small (a total of 140 cable systems, some with missing
prices) and the full list of explanatory variables above is too long, I tried various speci…cations for each regression
and only kept the most relevant variables. Also, notice that my license-fees data (from SNL Kagan [2007]) represents
national averages for all cable companies, so the cable company dummies help capture the di¤erences in actual fees
paid by di¤erent cable companies.
76 Notice that all 3 residuals enter m;k for each cable bundle. Also, the true speci…cation is not necessarily linear,
so a more ‡exible polynomial approximation can be used. Petrin and Train …nd that a simple linear speci…cation
works well for cable television.
77 Of course, all parameters are identi…ed from multiple moments, so I only refer to the most transparent link
between the moments and parameters.
78 Since covariances cov(Tj; Tkj:::)cannot be simulated in an unbiased way, in actual estimation I match E(Tj; Tkj:::)
instead. This applies to all the covariances in this subsection. Notice that E(Tjj:::); E (Tkj:::)are also matched, in
separate moments.
28
The second group is subscription choice moments. For base bundle choices (antenna; basic,
expanded-basic and digital cable; satellite), I match actual and predicted shares for each of the
base bundles, and covariances between bundle choices and: household demographics, bundle char-
acteristics (including price79) and interactions between bundle characteristics and demographics.
This pins down the parameters in bundle utility (5.3). For premium subscriptions, I match ac-
tual and predicted shares for each of the premium channels, and their correlations with household
demographics. This pins down the parameters in premium choice probability (5.4).
The third group of moments is covariances between viewing choices and bundle choices.
Speci…cally, for each of the base bundles, I match the covariance between viewing times and bundle
choices, and the covariances between viewing times and bundle characteristics interacted with bun-
dle choices. This captures consumers’self-selection into di¤erent bundles based on their unobserved
preferences80 . Similarly, I match the covariances between premium subscriptions and viewing time
for premium channels, which captures the self-selection into di¤erent premium subscriptions.
6.3. Additional Issues in Estimation and Identi…cation
Characteristics of cable bundles as instruments
Since I do not observe household’s exact location within the DMA, I specify the bundle char-
acteristics instruments for each household as the expected value of bundle characteristics computed
using the distribution of cable systems Pr(mhjXh)for this household. Notice that this expectation
varies across households, and it is correlated with their actual choices. For example, a household
that is more likely (based on its Xh) to live in areas with attractive cable bundles is also more likely
to subscribe to cable. Likewise, a household that is more likely to live in areas where channel jis
available on cable is more likely to watch this channel.
One concern might be that the expectation of bundle characteristics is a function of Xh;and
Xhalso a¤ects viewership and bundle choices directly, via the viewing utilities. However, notice
that the e¤ect of Xhon the expected bundle characteristics is a DMA-speci…c function (roughly,
an interaction between Xhand bundle characteristics for di¤erent locations within the DMA). In
contrast, the direct e¤ect of Xhvia the viewing utilities is the same for all DMAs.
Basic-only vs basic plus expanded-basic subscriptions
Simmons data does not distinguish between basic-only and basic plus expanded-basic sub-
scriptions (both are recorded as “analog cable”). In estimation, I compute choice probabilities
separately for basic (k= 2) and basic plus expanded-basic (k= 3), and match the combined proba-
bility for analog cable. To pin down the share of basic-only subscribers, I match the predicted share
of basic subscribers to its actual share from other sources (12% of all cable subscribers nationwide,
FCC [2005a]).
79 Notice that price and price residual are valid instruments in the control-function approach.
80 For example, consider two locations with identical channel lineups for all cable bundles and identical prices for
basic and expanded-basic cable, but di¤erent prices for digital basic. Conditional on demographics, the households
that choose digital cable in the more expensive location are those with higher unobserved preferences for TV-viewing,
thus the viewing time among digital-cable subscribers will be positively correlated with the price of digital cable.
29
One issue is that much of the variation for major cable channels is with respect to their
placement on basic vs expanded-basic tier (section 4.2). For example, if many consumers only
value ESPN (typically carried on expanded-basic), then the share of basic-only subscribers will be
much higher for the systems that o¤er ESPN on the basic tier. Since I do not observe basic-only
subscriptions separately from expanded-basic subscriptions, it could be a problem if the changes
in the basic-only share are mostly at the expense of expanded basic. However, this is unlikely to
be the case. Speci…cally, if ESPN is placed on basic cable (i.e. in order to get ESPN, consumer
has to pay around $18, compared to around $45 in the typical case), the basic-only share will also
increase at the expense of local antenna and at the expense of satellite.
Large number of moments in estimation
The number of moments in estimation is large (about 1100). The main reason is that I have a
large number of dependent variables for each household, speci…cally viewing times for 64 channels,
5 mutually-exclusive bundle alternatives, and choices for 6 premium channels. The large number
of moments can a¤ect the performance of GMM.
Hansen, Heaton and Yaron (1996) show that standard two-step GMM performs poorly when
the number of moments is large relative to the sample size. The reason is that the estimated optimal
weighting matrix for the second step is not estimated accurately if the number of moments is large.
Therefore, instead of using two-step GMM, I use less e¢ cient but more reliable one-step GMM (I
rescale the moments so that they are roughly the same order of magnitude).
Also, Stock and Wright (2000) and Phillips and Han (2006) show that GMM asymptotics can
break down when the number of moments is large but the moments are weak. However, the moment
conditions in my data are not weak. In particular, one set of moments matches the predicted and
actual average viewing times for the 64 channels, and the covariances between viewing choices and
a small number of key demographic variables (those demographics are highly signi…cant predictors
of viewing choices in preliminary reduced-form analysis). Another set of moments matches the
covariances of viewing times between channels (and the number of channels is …xed in asymptotics).
Another set of moments matches predicted and actual average subscriptions, and the covariances
between subscriptions and a small number of key demographics and cable package characteristics
(which are highly signi…cant predictors of subscriptions in reduced-form analysis). Koenker and
Machado (1999) point out that asymptotics can break down even with strong moments, if the
number of moments increases with the sample size. However, the number of moments in my case
is …xed and does not depend on sample size.
To evaluate the …nite-sample properties of my estimation procedure, I conducted several
Monte-Carlo estimation runs, generating arti…cial data and then estimating the full structural
model from this data. The point estimates and the reported standard errors were reasonable.
7. Empirical Results
The estimates are presented in table 4. First, I check whether the estimates are reasonable and
the …t of the model. Most of the parameters are of the expected sign. For example, ESPN utility
is signi…cantly higher for men, Lifetime for women, BET for African-Americans, while CNN util-
30
ity increases with age and education. Expected viewing utility has a signi…cant positive e¤ect on
bundle choices (b
0= 49:8 (8:4),b
1= 2:7 (0:9)). The units of expected utility are not directly inter-
pretable, so I check what happens to predicted subscriptions when I remove one channel from the
expanded-basic package, keeping the prices …xed81 . For example, when I remove CNN, expanded-
basic subscriptions drop by 6.1% (3.1 percentage points), and satellite gains 14.4% (2.8 percentage
points), with slight gains for local antenna and basic cable. The magnitudes for other major chan-
nels (Discovery, ESPN, TBS, TNT) are comparable, with a loss of 4.7%-6.8% for expanded-basic
and a gain of 11.4%-16.7% for satellite.
Own price elasticity is 2:09 for basic cable, 2:98 for expanded-basic and 2:21 for digital-
basic (average for all cable systems in the data), which is quite similar to the estimates in the
literature82. The closest substitute for expanded-basic cable is digital cable (with a cross price
elasticity of 1:58, vs 1:32 for satellite), and the closest substitute for digital cable is expanded-basic
cable (the cross price elasticity is 1:52, vs just 0:18 for satellite83). When I re-estimate the model
without controlling for price endogeneity, the estimates of price elasticities are much closer to zero
(1:1for basic, 1:3for expanded-basic and 0:05 for digital), as expected.
Next, I evaluate the …t of the model. I simulate subscriptions and viewing choices based
on the estimates, and compare them to the actual distributions in the data. In …gure 2, I plot
predicted and actual mean viewing time and proportion of non-zero time for each channel. The …t
is quite good. Next, the most important determinant of discriminatory e¤ects of bundling is the
covariance structure of channel preferences. In …gure 3, I plot predicted and actual covariances of
viewing times for each pair of the 64 channels in the data. The …t of covariances for the 32 most
popular channels (for which I estimate the factor-analytic component j!
h;i) is quite good. For
the fringe channels (for which I do not estimate j!
h;i), the …t of covariances is naturally less good,
but still reasonable. Besides covariances between individual channels, another useful measure is the
covariance between each channel and the rest of the channels combined (…gure 4). It determines
the e¤ect of including channel jin a typical bundle in the data. The …t is also quite good.
Next, I conduct counterfactuals to evaluate likely short-run e¤ects of unbundling for con-
sumers, cable networks and cable operators.
7.1. General Structure and Assumptions in Counterfactuals
Under bundling, consumers face two cable alternatives: (1) broadcast basic, which only contains
local broadcast channels, and (2) broadcast basic plus the cable bundle84 . The cable bundle contains
81 I compute it for a somewhat simpli…ed cable system, which o¤ers a broadcast-only basic and a “representative”
expanded-basic package, without a separate digital tier.
82 Some examples of estimates of own price elasticity for cable are: 2:19 in FCC (2002);3:22 in GAO (2003),
1:5for expanded-basic cable and 3:2for premium cable in Goolsbee and Petrin (2004).
83 Low substitution from digital cable to satellite is quite plausible. While the price of expanded-basic cable is
roughly the same as satellite, digital cable is substantially more expensive (even though its channel lineup is usually
inferior to satellite). Thus, consumers who get digital cable are those with disproportionately low preferences for
satellite relative to cable (for example, they cannot get satellite reception for physical reasons).
84 I do not include a separate digital tier (o¤ered by most cable systems and purchased by 35% of cable subscribers)
to be able to isolate the e¤ects of bundling in a more transparent setting. I include a separate basic tier (o¤ered by
31
all the non-premium channels o¤ered by at least half of all cable systems in the data (a total of 54
cable channels), and its channel lineup is somewhere between a typical expanded-basic and digital
bundle. Consumers can also choose local antenna or satellite.
My main unbundling scenario is “themed tiers”(section 7.2), in which I break up the cable
bundle into 7 mini-tiers by channel genre. This is one of the main unbundling scenarios advocated
by FCC (2006). All cable subscribers are required to get basic cable (all practical discussions of
unbundling include this requirement), plus they can get any combination of the mini-tiers. I also
consider an additional unbundling scenario, with mini-tiers based on channel owner (section 7.3).
I do not consider the option of full a la carte (unbundling to the level of individual channels)
for computational reasons. Speci…cally, while I can compute optimal a la carte subscriptions for each
household (given the prices), the resulting pro…t function is not di¤erentiable, due to discrete jumps
in households’subscriptions in response to a change in prices85 . This substantially complicates the
computation of optimal prices86 . Also, given the much higher complexity of choices under a la
carte, consumers’cognitive constraints might become important, making predictions less credible.
In unbundling counterfactuals, I assume that all cable alternatives (basic cable plus any
combination of the mini-tiers) have the same vertical constant , so the choice among them is
driven entirely by the di¤erences in expected viewing utilities and prices. I set their equal to
the of the most popular bundle in the data (basic plus expanded-basic). This amounts to some
improvement in the for basic cable relative to the estimates. However, such an improvement is
quite plausible under unbundling. In particular, from conversations with industry executives, many
cable systems strive to make basic cable as invisible as possible, steering consumers’ attention to
other, more expensive bundles87 . Under unbundling, consumers’choices are framed explicitly as
basic cable plus any combination of the mini-tiers, so the visibility of basic cable is likely to be
much higher. To make the outcomes under bundling comparable to the unbundled case, I set the
for basic cable at the same level.
In all cases, I assume that satellite does not react to cable unbundling, i.e. satellite packages
and prices remain the same. Since satellite packages and prices are the same nationwide, this
assumption is plausible for counterfactuals a¤ecting relatively few cable systems. Notice that cable
operators make many decisions at the local level, so counterfactuals focusing on an individual system
are meaningful. In principle, I could conduct counterfactuals with re-optimization by satellite,
however the results would be much less credible due to data limitations88 .
almost all systems in the data) because in all practical discussions of unbundling (e.g. FCC [2006]), consumers will
still be required to get basic cable, i.e. unbundling only applies to tiers above basic.
85 For mini-tiers, I use a di¤erent computational approach, in which I explicitly compute (di¤erentiable) choice
probabilities for each possible combination of the mini-tiers. However, this approach is not feasible for a la carte,
since there are 254 1:81016 possible combinations of channels.
86 It requires nonlinear search in 55 dimensions, with a non-di¤erentiable objective function. I tried using the
Nelder-Mead method, which does not rely on derivatives, however it tends to get stuck in local maxima, and the
results are highly sensitive to the starting values. One feasible option is to constrain all channel prices to be the
same. The main results in this case are similar to those for mini-tiers.
87 Notice that regulation forces them to o¤er relatively cheap basic packages at regulated prices. As a result, basic
cable is rarely mentioned in their advertising. Also, it app ears that their sales representatives are often instructed to
not mention the option of a basic-only subscription at all, unless explicitly asked about it.
88 For satellite subscribers in the data, I do not observe whether they get DirecTV or Dish, therefore the substitution
32
I focus on a “representative”cable system for the 4 DMAs used in estimation (Boston, Los
Angeles, New York and San Francisco), i.e. the distribution of demographics in the area served
by the system is the same as in those 4 DMAs combined. I assume that this cable system is
not vertically integrated with any of the cable networks, i.e. it does not internalize the e¤ect of
its decisions on the networks’revenues. Besides license fees to the networks, I assume that the
cable operator has an additional marginal cost of $3 a month per subscriber (this covers a typical
franchise fee and fees to broadcast networks), and all other expenditures are …xed costs.
Unbundling might increase cable operator’s equipment and customer-service costs (for exam-
ple, see Booz, Allen, Hamilton [2004] for reasons why they will increase substantially, or FCC [2006]
for reasons why they will not). Since it is not clear how much they will increase (and whether it
will a¤ect marginal or …xed costs), I assume that these costs do not increase at all (the best-case
scenario for unbundling).
Besides subscription fees, cable operators get revenues from their share of advertising time
($4.60 a month per subscriber on average, FCC [2005a]). I do not have data on cable networks’
advertising rates (also, the local advertising rates relevant for cable operators might be systemat-
ically di¤erent from the networks’national rates). I divide $4.60 by the average viewing time to
get a rough estimate of advertising revenue per viewer-hour, which enters cable operator’s pro…t-
maximization89. This approximation is obviously quite crude, however advertising accounts for a
small share of cable operators’revenues.
In counterfactuals, cable operator chooses the optimal retail prices for basic cable and each
mini-tier (or full bundle under bundling), taking the structure of its programming costs (license fees)
as exogenously given. As discussed in section 2.2, networks’license fees per subscriber are likely
to increase a lot after unbundling. In unbundling counterfactuals, …rst I compute the outcomes for
the original license fees in the data (SNL Kagan [2007]), and then for several alternative scenarios
on programming costs.
Sanity check: Bundling benchmark
I present predicted outcome under bundling (for actual license fees in the data) in column
(a) of table 6. The optimal prices are $23.13 for basic cable (vs $18.08 on average in the data),
and $46.64 for the full bundle (vs $45.32 for basic + expanded-basic in the data). Predicted
market shares are 7.0% basic-only cable (same as in the data), 47.3% full cable bundle (vs 50% for
expanded-basic cable and above in the data), 24.4% antenna (vs 23%) and 21.3% satellite (vs 20%
in the data). With the exception of basic price (which is often regulated), predicted outcomes are
reasonably close to the actual ones. This is encouraging for the estimates (especially in the light of
possible concerns about the control-function correction for price endogeneity). Notice that I do not
use any supply-side conditions in estimation, and I have data on marginal costs (so I do not have
patterns between the two satellite providers are not identi…ed from the data. Also, since there is no price variation
for satellite, its price elasticity is identi…ed only through functional form assumptions.
89 Notice that cable operator maximizes pro…ts from both subscriptions and advertising, so advertising a¤ects its
optimal choice of retail prices. When computing the optimal prices, for each price vector I explicitly compute not
only predicted subscriptions but also viewership by cable subscribers, which determines cable operator’s advertising
revenues.
33
to back them out from supply-side conditions), so nothing in the estimation procedure arti…cially
forces them to be close.
7.2. “Themed tiers”
In this counterfactual, I break up the cable bundle into 7 “themed tiers” by channel genre (see
table 5a for channel lineups). First, I compute the “themed tiers”outcome for the original license
fees in the data. Although the license fees are unlikely to remain the same, it provides a natural
starting point. The results are in column (a) of tables 7a, 7b, and the parallel bundling outcome is
in column (a) of table 6.
The optimal prices of the mini-tiers range from $0.20 for women’s programming to $6.97
for “general entertainment”and $9.27 for sports90. Notice that all mini-tier subscribers also have
to pay the basic fee, which is quite substantial ($29.01, vs $23.13 under bundling). 54.3% of
consumers get at least one mini-tier (vs 47.3% getting the full bundle under bundling). Mini-
tiers gain subscribers at the expense of local antenna (which loses 0.7 percentage points relative to
bundling), basic-only cable (3.9 percentage points) and satellite (2.4 percentage points). On average,
cable subscribers get 3.5 mini-tiers out of 7.91 The most popular tiers are “general entertainment”
(70% of cable subscribers), “education/learning” (66%) and “news/information” (60%), and the
least popular ones are “movies”(26%) and “sports”(27%). For comparison, under bundling 87%
of cable subscribers were getting the full bundle, so the networks on all mini-tiers lose subscribers.
Cable operator’s pro…ts increase by 16% after unbundling. The main reason for this is a
sharp decrease in the total cost of cable programming. For example, the cost of license fees for the
sports channels is $4.33 per subscriber. Under bundling, the cable operator incurs this cost for 87%
of its subscribers (all the full-bundle buyers), but under “themed tiers”, only for the 27% who get
the “sports”tier. Unbundling reduces cable operator’s average programming costs per subscriber
from $11.29 to $6.43. At the same time, average revenue per subscriber drops by just $1.34, and
the total number of subscribers increases slightly.
The main outcomes I focus on are (1) the welfare e¤ects for consumers (since the push for
unbundling is based on the argument that consumers would gain a lot from unbundling) and (2)
the outcomes for the networks (since one of the main concerns about unbundling is that it can
destroy the economic foundations of the cable networks).
Outcomes for consumers
The average cable bill for mini-tier subscribers (excluding basic-only subscribers) is $43.03,
7.7% less than the original price of the full bundle. However, they also get fewer channels (half of
90 Notice that a change in the price of a mini-tier a¤ects not only its own subscriptions, but also subscriptions to
basic cable and other mini-tiers. As a result, the optimal retail markups di¤er across the mini-tiers. Furthermore, in
some cases (e.g. “women’s programming”) the optimal markup is negative. The reason is that lowering the price of
this mini-tier generates additional revenues from basic cable and other mini-tiers.
91 I also computed the mixed-bundling scenario, in which consumer can get either mini-tiers or the full bundle (at
a discount relative to the unbundled prices). However, the optimal discount on the full bundle is small, and very few
consumers choose the full bundle. Thus, the mixed-bundling outcome is almost identical to the “pure unbundling”
case analyzed in this section.
34
the mini-tiers on average), which may reduce welfare (in particular, the original bundle subscribers
no longer get the mini-tiers they value at above zero but below the unbundled price). Also, the
price of basic cable goes up, hurting basic-only subscribers. On the other hand, mini-tiers attract
new consumers who were not getting cable programming under bundling (original local-antenna
and basic-only subscribers), which may increase welfare. By explicitly linking bundle choices to
viewing utilities, the model allows me to measure the combined e¤ect of the change in prices and
the change in access to cable programming. On average, consumers gain slightly from the switch
to “themed tiers”, but the average increase in consumer surplus is just 35 cents per household
per month92 . Notice that this is a best-case scenario for unbundling: cable operator’s equipment
and customer-service costs do not go up, and the networks do not increase their license fees per
subscriber. Thus, even the best-case gains to consumers are minimal.
One reason consumers do not save much from unbundling is that the optimal basic fee is
$29.01, almost two-thirds of the original bundle price. In practice, the price of basic cable is often
regulated. In column (b) of tables 6, 7a, 7b, I impose price regulation, setting the price of basic
cable at $15. Cable operator responds to price regulation by charging higher prices for the mini-
tiers. The gain in consumer surplus (relative to bundling with the same price regulation) is still
minor, 73 cents per household per month. Also, notice that the cable operator can easily bypass
the regulation of basic fees (for consumers getting at least one mini-tier), for example by requiring
all mini-tier subscribers to get a converter for an extra fee93. Therefore, I do not impose price
regulation in the rest of counterfactuals.
Outcomes for the networks
The outcomes for the networks (without price regulation) are in column (a) of table 7b. Cable
networks have two sources of revenue, license fees per subscriber and advertising, coming from
both cable and satellite. As mentioned earlier, I assume that unbundling only applies to cable (not
satellite), so …rst I focus on the outcomes for the networks only among cable subscribers. Although
cable gains market share after unbundling, the networks on all mini-tiers lose subscribers, dropping
by 40% on average. This is not surprising, since most consumers watch only a small fraction of
the channels in the bundle. Despite the sharp drop in the number of subscribers, viewership (i.e.
advertising revenue) among cable subscribers increases slightly, by 1%. Networks’total revenues
from cable (license fees and advertising) drop by 18% on average94.
92 I measure welfare changes for each household as the change in expected bundle utility (Efmaxgfor equation
(5.3)), divided by the price coe¢ cient. In computing this change for each household, I hold the unobservables !h; ! P
h
constant, but integrate out the draws of the logit shocks h;f (since they vary across di¤erent bundle choice occasions,
unlike !h; !P
h).
In many cases, an important concern about empirical welfare measures is that we do not observe consumers’
maximum willingness to pay for a given good (e.g. see the discussion in Goolsbee and Petrin [2004]). However, this
concern does not apply in my case, since after unbundling consumers still have access to the same set of channels,
and the only thing that changes is the prices they face for di¤erent combinations of channels.
93 The price of basic cable (including fees for any equipment required to receive it) is regulated on public-interest
grounds, to give consumers access to the broadcast networks at a¤ordable prices. This justi…cation would not apply
to additional equipment required to receive the mini-tiers.
94 I do not have data on networks’ advertising rates. On average, networks get about half of their revenues from
advertising and half from license fees. I assume that the advertising revenue per viewer-hour is the same for all
networks (which is clearly a crude approximation). The rate per viewer-hour is calibrated so that advertising accounts
35
All mini-tiers lose revenue, but some are a¤ected much more than others. The least-a¤ected
tiers are “general entertainment”and “education/learning”, which still see a 15-20% drop in the
number of subscribers, combined with slight increases in viewership (1-3%). The worst-hit tier is
“sports”, with a drop of 68% in the number of subscribers and a 19% drop in viewership. The main
reason is that sports channels have disproportionately high license fees (they account for 33% of
the total license fees but just 10% of viewership –table 5a), so the retail price of the sports tier
($9.27) is high enough to exclude many of the occasional sports viewers. Satellite share declines
slightly after unbundling, so networks’revenues from satellite also drop a little.
Thus, if the networks’license fees per subscriber do not increase at all after unbundling,
consumers would bene…t slightly in the short run, but the networks’revenues would decline sub-
stantially, reducing their ability to invest in programming (which is likely to harm consumers in
the long run).
7.2.1. Proportional increase in the license fees
Given the decline in the number of subscribers after unbundling, the networks are likely to increase
their license fees (per subscriber). For simplicity, I assume that the networks increase their license
fees proportionally to the drop in their subscriber counts above. After that, the cable operator
re-optimizes its retail prices taking the new license fees as given. Notice that such an increase in
the license fees does not fully compensate the networks, since the number of subscribers will decline
further after the re-optimization. Still, it provides a simple lower bound on how much the license
fees would have to increase in order to keep the networks’revenues constant.
The results are in column (e) of tables 7a, 7b. The optimal prices of most mini-tiers go
up95 . The largest price increase is for the sports tier, because its license fees more than triple (from
$4.33/sub to $13.37/sub). Its retail price increases from $9.27 to $37.01, and sports tier subscrip-
tions drop from 27% to 11% of all cable subscribers. Networks’revenues are still substantially
lower than they were under bundling. The increase in the license fees partially o¤sets the loss of
subscribers, but higher retail prices also reduce viewership and advertising revenues (especially for
the sports tier).
Consumers are worse o¤ than they were under bundling, with average consumer surplus
dropping by $2.43 per household per month.
7.2.2. Alternative License Fee Arrangements
>From the results above, unbundling is likely to substantially reduce networks’license-fee revenues.
Also, if the networks increase their fees per subscriber to try to o¤set the loss of subscribers, it
creates substantial ine¢ ciency due to double-marginalization (notice that the cable operator adds
its retail markup without coordination with the networks).
for 50% of the networks’ total revenue in the original bundling outcome.
95 The optimal prices of some mini-tiers decline, even though their license fees went up. This is not unreasonable,
since the price of each mini-tier also a¤ects revenues from basic fees and other mini-tiers.
36
One way to preserve networks’revenues without creating substantial ine¢ ciency is to replace
the fee-per-subscriber scheme with an alternative arrangement, such as lump-sum fees or revenue-
sharing.
Equivalent lump-sum fees
Since the networks’ marginal costs per subscriber are zero, the most natural alternative
arrangement is lump-sum payments. I assume that the cable operator pays each network a lump-
sum fee equal to its license-fee revenues under bundling. The results are in column (c) of tables 7a,
7b.
The optimal prices of most mini-tiers decrease somewhat (because the marginal costs are
now zero), and the optimal price of basic cable increases to $30.12. 57.4% of consumers now
get mini-tiers (vs 54.3% under the original license fees). The most popular mini-tiers are still
“general entertainment” (74% of all cable subscribers) and “education/learning” (64%), and the
least popular tiers are still “movies”(28%) and “sports” (36%). Cable operator’s pro…ts are 4.3%
higher than in the original bundling outcome (column (a) of table 6). Since the total cost of cable
programming is the same in both cases, this suggests that the cable operator can extract surplus
more e¤ectively via unbundled sales (i.e. the discriminatory e¤ects of bundling are relatively weak).
Average consumer surplus increases by $1.63 per household per month, relative to the original
bundling outcome. However, these gains are not due to unbundling per se, but entirely due to the
switch to a more e¢ cient upstream arrangement. When I compute the bundling outcome for a
similar lump-sum arrangement, the gain in consumer surplus is even larger, $1.89 (column (c) of
table 6).
Compared to the original bundling outcome, total viewership for the cable networks is slightly
higher (column (c) of table 7b). Their license-fee revenues from the cable operator are the same as
before, but they get lower revenues from satellite since its market share is now lower (recall that
I assume that satellite keeps o¤ering its original bundle at its original price, and it stays with the
original license-fee arrangement). As a result, networks’total revenues drop by 2%.
This scenario bene…ts consumers without signi…cantly harming the networks. However, as
mentioned above, these gains are driven by the elimination of double-marginalization, and the
net e¤ect of unbundling itself is negative. Thus, the main way in which unbundling can bene…t
consumers is by forcing the industry to switch to more e¢ cient lump-sum arrangements. For
some reason, the industry does not use such arrangements now (with a few exceptions96), however
unbundling might make them relatively more attractive for the networks. Also, if cable operator’s
equipment and customer-service costs increase after unbundling, the gains from a switch to lump-
sum fees combined with unbundling would be lower than found above.
Revenue-sharing
Another alternative is to use revenue-sharing. The most natural revenue-sharing scheme,
paying the networks a …xed percentage of the revenue from their mini-tier, is vulnerable to manip-
ulation. Speci…cally, cable operator optimally sets the mini-tier prices close to zero (to reduce the
96 SNL Kagan (2007) reports several recent lump-sum deals between one of the satellite operators and some of the
cable networks (without mentioning any speci…c names).
37
payments to the networks), and increases the basic fee (which is not shared with the networks97)
to o¤set the low mini-tier prices. Therefore, I use a simpler arrangement. Each network gets the
same share of total subscription revenue (from all mini-tiers and basic fees) as it was getting from
license fees in the original bundling outcome.
The results are in column (d) of tables 7a, 7b. The outcomes for consumers, cable operator
and the networks are quite similar to those for the lump-sum fees. Compared to the original
bundling outcome, average consumer surplus increases by $1.90 per household, slightly higher than
in the lump-sum case98 . However, again the gains are due to the switch to a more e¢ cient license-
fee arrangement, not due to unbundling. When I compute the parallel outcome under bundling,
the gain in consumer surplus is even higher. Thus, again the main way in which unbundling can
bene…t consumers is by forcing the industry to switch to a more e¢ cient upstream arrangement.
7.3. Mini-Tiers by Owner
Another plausible way to structure the mini-tiers is based on channel ownership. Speci…cally, I set
up a separate mini-tier for the channels owned by each of the 5 largest media companies (Disney,
Time Warner, News Corp., NBC Universal, Viacom), plus another mini-tier for the remaining
channels (see table 5b for channel lineups). This scenario might be easier to implement than
“themed tiers”, because it does not require breaking up the existing wholesale bundles of channels99.
The results are in tables 8a, 8b.
The main results are similar to those for the “themed tiers”. For the original license fees in the
data (column (a)), consumers gain slightly from unbundling, but the increase in consumer surplus
is just 31 cents per household per month. All mini-tiers lose subscribers, so the networks’license-fee
revenues drop substantially. On the other hand, viewership (advertising revenue) does not change
much. If the networks increase their license fees proportionally to the loss of subscribers (column
(e)), unbundling would reduce the average consumer surplus. Consumers gain if unbundling is
combined with a lump-sum or revenue-sharing arrangement in the upstream market (columns (c)
and (d)), but the net e¤ect of unbundling is minimal, and most of the gains are due to the switch
to a more e¢ cient upstream arrangement.
7.4. Discriminatory E¤ects of Bundling
Discriminatory e¤ects of bundling are one of the main theoretical explanations for the widespread
use of bundling, and one of the main mechanisms through which unbundling might bene…t con-
sumers. To isolate the discriminatory e¤ects of bundling, I set cable operator’s advertising revenues
to zero and the license fees to zero. This way, I can check whether bundling facilitates surplus
97 Since most consumers buy multiple mini-tiers, there is no clear way to link basic-fee revenues to a speci…c
mini-tier.
98 The reason is that revenue-sharing only applies to subscription revenues but not to advertising revenues, so cable
operator chooses slightly lower prices (relative to the lump-sum case) to increase its advertising revenues. I do not
share cable operator’s advertising revenues because they are harder for the networks to observe.
99 Also, while each “themed tier”combines content from multiple owners, this scenario puts content owners in direct
competition with each other, which may amplify their incentives to invest in programming in the long run.
38
extraction by the …rm, relative to unbundled sales (this is the main comparison used in the the-
oretical models of discriminatory e¤ects, e.g. Adams and Yellen [1976]). Cable operator’s pro…ts
under bundling are 1.7% lower than under “themed tiers”, and 1.5% lower than under mini-tiers
by owner. I also compute the full a la carte outcome (unbundling to the level of individual chan-
nels), constraining all channel prices to be the same. This provides a lower bound on a la carte
pro…ts with channel-speci…c optimal prices (as discussed earlier, for computational reasons I cannot
…nd optimal channel-speci…c prices). The pro…ts under full a la carte are also higher than under
bundling. Thus, bundling does not facilitate surplus extraction by the …rm relative to unbundled
sales. The main reason for this is that consumers’bundle valuations are still quite heterogeneous.
For example, when the only cable alternative is the full bundle (for simplicity), the 40th percentile
of bundle valuations is $39.6 and the 50th percentile is $46.9 (18% higher). This heterogeneity
constrains the …rm’s ability to extract surplus via bundling.
7.5. Discussion
My main …nding is that consumers do not gain much from unbundling, even in the best-case
scenarios. As discussed earlier, unbundling could bene…t consumers in several ways. First, by
eliminating the discriminatory e¤ects, it could reduce cable operator’s ability to extract surplus
from consumers. However, the discriminatory e¤ects of bundling turn out to be weak, so unbundling
does not redistribute surplus from cable operator to consumers. Second, it might increase consumer
surplus by partially serving consumers who were not getting cable programming under bundling
(local-antenna and basic-only households). Depending on a speci…c scenario on programming costs,
the share of local-antenna and basic-only households declines by 0.6-8.7 percentage points after
unbundling, i.e. a small but non-negligible number of additional consumers are getting served.
However, these are consumers with relatively low preferences for cable programming, so the welfare
gains for them are quite minor. Third, unbundling might reduce the total wholesale cost of cable
programming, and consumers might bene…t from this cost reduction. In some scenarios, unbundling
in fact substantially reduces cable operator’s programming costs, however very little of this cost
saving is passed on to consumers. Thus, the three e¤ects of unbundling that could potentially
bene…t consumers in the short run turn out to be small in the data. Also, unbundling might
reduce consumer surplus by ine¢ ciently excluding some of the original bundle buyers (for example,
occasional sports viewers who value the sports tier at less than its unbundled price). At least for
the sports tier, the exclusion of occasional viewers is quite important (at the original license fees,
viewership of the sports tier drops by 19% after unbundling), reducing consumer surplus.
One potentially important assumption in counterfactuals is that satellite does not react to
cable unbundling, i.e. it keeps o¤ering its original bundle at the original price (as discussed in
section 7.1, counterfactuals with re-optimization by satellite would be much less credible due to
data limitations). If satellite reacts to cable unbundling by switching to unbundled sales itself,
39
the gains to consumers might be larger100;101. Also, cable accounts for almost three quarters of
all cable/satellite subscribers in my data (its share is 57% vs 20% for satellite). Thus, my results
indicate that unbundling three quarters of the market would not bene…t consumers much102.
8. Conclusion
Concerns over cable companies’bundling practices and rapid increases in cable prices have sparked
an active policy debate about retail unbundling, i.e. requiring cable companies to o¤er subscriptions
to “themed tiers”or individual channels on a la carte basis. Supporters of unbundling policies argue
that a switch to unbundled sales would substantially bene…t consumers, while opponents argue that
it would increase cable prices and destroy the economic foundations of the cable networks.
In this paper, I develop an empirical model of demand for cable bundles and viewership, and
use it to analyze the short-run e¤ects of unbundling policies for consumers, cable operators and
cable networks. By tying together consumers’purchases of bundles and their subsequent viewing
choices for individual channels, the model allows me to identify how much consumers are willing to
pay for each channel, and to predict their subscriptions and viewership in out-of-sample unbundling
counterfactuals. I estimate the model using individual-level data on cable/satellite subscriptions
and viewing choices for 64 main cable channels. I use the estimates to simulate the e¤ects of
unbundling policies (primarily “themed tiers”), for several alternative scenarios on the wholesale
prices of cable programming after unbundling.
I …nd that consumers do not gain much from unbundling. Even if the networks do not increase
their wholesale prices (license fees per subscriber) after unbundling, the average short-run increase
in the consumer surplus is estimated at just 35 cents per household per month. The networks
experience a sharp drop in the number of subscribers (but not in viewership), which substantially
reduces their revenues. As a result, the networks are likely to increase their license fees and/or
cut their investment in programming. If they increase their license fees to try to o¤set the loss of
subscribers, consumer surplus is estimated to decrease after unbundling.
100This is not necessarily the case. For example, Nalebu¤ (2000) …nds that price competition in the “bundle vs
bundle” case is more intense than in case of “components vs components” or “bundle vs components”.
101Alternatively, consumers could gain if satellite reduced the price of its bundle in response to cable unbundling.
However, when I compute satellite’s best response to the mini-tier prices from column (a) of table 7 (assuming that
satellite does not unbundle), the optimal price of the satellite bundle is slightly higher than in the original bundling
outcome. In this computation, I treat the two satellite providers as a single …rm, and calibrate its marginal costs
so that the actual price of the satellite bundle is optimal. (However, as mentioned above, this computation is less
credible than my main counterfactuals).
102In a parallel pap er, Crawford and Yurukoglu (2008) …nd that unbundling substantially bene…ts consumers.
Besides di¤erences in the data and mo del speci…cation, several factors might account for the di¤erence in our main
results. First, they focus on full a la carte (unbundling to the level of individual channels). Second, they compute
Nash equilibrium in which all 3 …rms (cable and two satellite providers) switch to a la carte. To do that, they have
to impose additional assumptions, since the main data limitations for satellite in their case are similar to mine (see
section 7.1). Finally, their estimates of viewing preferences do not account for self-selection, due to data limitations.
In my estimates, this self-selection turns out to be important, i.e. non-subscribers (local-antenna and basic-only
households) have much lower unobserved viewing preferences. Thus, if one did not control for self-selection in my
data, the model would heavily overpredict unbundled subscriptions and welfare gains from unbundling for current
non-subscribers.
40
One scenario that would bene…t consumers (without hurting the networks) is to combine
unbundling with a switch to a more e¢ cient lump-sum or revenue-sharing arrangement in the
upstream market. In this case, consumer surplus increases by $1.63-$1.90 per household. However,
a switch to a similar upstream arrangement without unbundling would bene…t consumers even
more, i.e. the net e¤ect of unbundling itself is still negative.
The push for unbundling by FCC and consumers’ organizations is based on the argument
that it would substantially bene…t consumers. My …ndings do not support this argument. A
potentially more fruitful area for regulatory intervention could be in the upstream market for cable
programming, to be explored in future research.
References
[1] Adams, W., and J. Yellen (1976): “Commodity Bundling and the Burden of Monopoly,”The
Quarterly Journal of Economics, 90(3), 475–498.
[2] Bakos, Y., and E. Brynjolfsson (1999): “Bundling Information Goods: Pricing, Pro…ts, and
E¢ ciency,”Management Science, 45(12), 1613–1630.
[3] Bakos, Y., and E. Brynjolfsson (2000): “Bundling and Competition on the Internet,”Marketing
Science, 19(1), 63–82.
[4] Ben-Akiva, M. (1973): “Structure of Passenger Travel Demand Models,”PhD thesis, Depart-
ment of Civil Engineering, MIT, Cambridge, MA.
[5] Berry, S., J. Levinsohn, and A. Pakes (1995): “Automobile Prices in Market Equilibrium,”
Econometrica, 60(4), 889–917.
[6] Berry, S., J. Levinsohn, and A. Pakes (2004): “Estimating Di¤erentiated Product Demand
Systems from a Combination of Micro and Macro Data: The Market for New Vehicles,”
Journal of Political Economy, 112(1), 68–105.
[7] Berry, S., and A. Pakes (2007): “The Pure Characteristics Model of Demand,” International
Economic Review, 48(4), 1193–1225.
[8] Booz, Allen, Hamilton (2004): “The a la Carte Paradox: Higher Consumer Costs and Reduced
Programming Diversity,” discussion paper, Booz Allen Hamilton, Report prepared for the
National Cable & Telecommunications Association.
[9] Chipty, T. (2001): “Vertical Integration, Market Foreclosure, and Consumer Welfare in the
Cable Television Industry,”American Economic Review, 91(3), 428–453.
[10] Chu, C., P. Leslie, and A. Sorensen (2006): “Incomplex Alternatives to Mixed Bundling,”
mimeo, Stanford University.
[11] Crawford, G. (2008): “The Discriminatory Incentives to Bundle in the Cable Television In-
dustry,”Quantitative Marketing and Economics, 6, 41–78.
41
[12] Crawford, G., and A. Yurukoglu (2008): “The Welfare E¤ects of Bundling in Multi-Channel
Television Markets,”mimeo, University of Arizona.
[13] Elrod, T., and M. Keane (1995): “A Factor-Analysis Probit Model for Representing the Market
Structure in Panel Data,”Journal of Marketing Research, 32(1), 1–16.
[14] FCC (2004): “Report on the Packaging and Sale of Video Programming to the Public,”Dis-
cussion paper, Federal Communications Commission.
[15] FCC (2005a): “Eleventh Annual Report on the Status of Competition in the Market for the
Delivery of Video Programming (2004 Report),” Discussion paper, Federal Communications
Commission.
[16] FCC (2005b): “2004 Report on Cable Industry Prices,” Discussion paper, Federal Communi-
cations Commission.
[17] FCC (2006): “Further Report on the Packaging and Sale of Video Programming to the Public,”
Discussion paper, Federal Communications Commission.
[18] GAO (2003): “Issues Related to Competition and Subscriber Rates in the Cable Television
Industry,”Discussion paper, General Accounting O¢ ce.
[19] Goolsbee, A., and A. Petrin (2004): “The Consumer Gains from Direct Broadcast Satellites
and the Competition with Cable TV,” Econometrica, 72(2), 351–381.
[20] Han, C., and P. Phillips (2006): “GMM with Many Moment Conditions,”Econometrica, 74(1),
147–192.
[21] Hansen, L., J. Heaton, and A. Yaron (1996): “Finite Sample Properties of Some Alternative
GMM Estimators,”Journal of Business and Economic Statistics, 14, 262–280.
[22] Ho, K. (2006): “The Welfare E¤ects of Restricted Hospital Choice in the US Medical Care
Market,”Journal of Applied Econometrics, 21, 1039–1079.
[23] Ho, J., K. Ho, and J. Mortimer (2008): The E¤ects of Full-Line Forcing Contracts”, mimeo
[24] Johnson, N., S. Kotz, and N. Balakrishnan (1995): “Extreme Value Distributions,” in N.
Johnson, S. Kotz, and N. Balakrishan, eds., Continuous Univariate Distributions, vol. 2, 2d
ed. New York: JohnWiley & Sons, Inc.
[25] Koenker, R., and J. Machado (1999): “GMM Inference when the Number of Moment Condi-
tions is Large,”Journal of Econometrics, 93, 327–344.
[26] McAfee, R., J. McMillan, and M. Whinston (1989): “Multiproduct Monopoly, Commodity
Bundling, and Correlation of Values,”The Quarterly Journal of Economics, 104(2), 371–383.
[27] Nalebu¤, B. (2000): “Competing Against Bundles,” mimeo, available at
http://ssrn.com/abstract=239684.
42
[28] Nalebu¤, B. (2003): “Bundling, tying, and portfolio e¤ects,”Department of Trade and Indus-
try working paper.
[29] Nalebu¤, B. (2004): “Bundling as an Entry Barrier,” The Quarterly Journal of Economics,
119(1), 159–187.
[30] Petrin, A., and K. Train (2006): “Control Function Corrections for Unobserved Factors in
Di¤erentiated Product Models,”mimeo
[31] Salinger, M. (1995): “A Graphical Analysis of Bundling,” The Journal of Business, 68(1),
85–98.
[32] Sargent, T., and C. Sims (1977): “Business Cycle Modeling without Pretending to Have Too
Much A Priori Economic Theory.”In New Methods in Business Cycle Research: Proceedings
from a Conference, 45–109, Minneapolis: Federal Reserve Bank of Minneapolis.
[33] Schmalensee, R. (1984): “Gaussian Demand and Commodity Bundling,”The Journal of Busi-
ness, 57(1), S211–S230.
[34] SNL Kagan (2007): Cable Program Investor, no. 114, May 31, 2007.
[35] Song, M. (2008): “A Hybrid Discrete Choice Model of Di¤erentiated Product Demand with
an Application to Personal Computers,”mimeo.
[36] Stigler, G. (1963): “United States v. Loew’s Inc.: A Note on Block-Booking,” The Supreme
Court Review, 1963, 152–157.
[37] Stock, J., and M. Watson (1999): “Forecasting In‡ation”, Journal of Monetary Economics,
44, 293–335.
[38] Stock, J., and M. Watson (2002): “Macroeconomic forecasting using di¤usion indexes,”Journal
of Business and Economic Statistics, 20, 147–162.
[39] Stock, J., and J. Wright (2000): “GMM with Weak Identi…cation,” Econometrica, 68(5),
1055–1096.
[40] Whinston, M. (1990): “Tying, Foreclosure and Exclusion,”American Economic Review, 80(4),
837–859.
[41] Wolak, F. (2003): Discussant Comments for NBER Winter IO Program Meeting, February 8,
2003. Discussion of Petrin A. and K. Train (2002), “Omitted Product Attributes in Discrete
Choice Models”.
43
9. Appendix A. Computation and Simulation Details in Estimation
Integrating out the unobservables
In computing all the expectations below, I integrate out the unobserved heterogeneity wh
(wh;1; :::; wh;Kh; wp
h)and the unobserved cable system mhas follows:
E(YjXh; ) = X
mhZwh
E(YjXh; wh; mh; )dF (whj)P r(mhjXh)
where Ystands for any dependent variable in the model, and denotes the vector of parameters
(the distribution of cable systems Pr(mhjXh)was computed earlier as described in section 5.3, so
it does not depend on ).
I simulate it by drawing Rdraws of whsF(whj)and mhsP r(mhjXh);and replacing the
expectation with the simulation average
E(YjXh; )1
RX
r=1:::R
E(YjXh; wh;r; mh;r ; )
where wh;r; mh;r denote the r’th draw. This simulator is unbiased.
Below, I present the computation of the relevant expectations conditional on a speci…c draw
of wh; mh:To reduce the notation, I use E(:::j)as a shorthand for E(:::jXh; wh; mh; ).
Computing subscription probabilities
I compute predicted probabilities …rst for the base bundles Baseh, and then for premium
channels P remh;j .
First, for each household member, I compute viewing utilities for each channel using equation
(5.1). Next, I compute her expected viewing utility for each of the available base bundles using
equation (5.2). Then, for each household, I compute subscription-stage utility for each base bundle
kusing equation (5.3).
This gives me the choice probabilities for the base bundles. The provider-speci…c shocks
h;f(k)are i.i.d. logit shocks for antenna, cable and satellite. Since the same h;f (k)applies to all
the cable bundles (k= 2:::4), and there are no additional unobservables (for a given value of wh),
only one cable bundle will be chosen with positive probability. Speci…cally,
Pr(Baseh=kj) = eVh;k
eVh;1+eVh;5+ maxfeVh;2; eVh;3; eVh;4gfor k= 1;5
Pr(Baseh=kj) = eVh;k IfVh;k = maxfVh;2; V h;3; V h;4g
eVh;1+eVh;5+ maxfeVh;2; eVh;3; eVh;4gfor k= 2;3;4
where Vh;k Vh;k h;f(k)from equation (5.3). Notice that Vh;k is a function of Xh; wh; and the
characteristics of base bundle kfor cable system mh.103
103In estimation, I compute choice probabilities using an importance sampler that follows Song (2008). Speci…cally,
44
Choice probabilities for premium channels depend on Baseh(local-antenna households cannot
get premium channels, and some cable systems do not o¤er some of the premium channels). I
compute subscription probability for premium channel jas
Pr(P remh;j j) = X
k=2:::5
Pr(P remh;j j; Baseh=k)Pr(Baseh=kj)
where Pr(P remh;j j; Baseh=k)is computed using equation (5.4).
Computing predicted viewing outcomes
The viewing decisions depend on household’s subscription Baseh; P remh;because it deter-
mines which channels consumers can watch at home. Conditional on Baseh; P remh(and Xh; wh;
mh; );individual i’s viewing probability for channel jin each period, Pr(i; jj; Baseh; P r emh);
follows standard multinomial logit, with utilities computed using equation (5.1). I use it to compute
several kinds of predicted viewership outcomes.
The …rst outcome is E(Th;i;j IfBaseh=kgj);computed for each j; k.104 It is equal to
E(Th;i;j IfBaseh=kgj) =
= Pr(Baseh=kj)0
@X
P remh
E(Th;i;j j; Baseh=k ; P remh) Pr(P remhj; Baseh=k)1
A
where the summation over P remhgoes over all possible combinations of the premium channels
(given Baseh), and
E(Th;i;j j; Baseh; P r emh) = TPr(i; jj; Baseh; P remh)
where Tis the total number of periods.
I also compute the probability of watching channel jat least once during the week, E(IfTh;i;j >
0g IfBaseh=kgj):The computation is similar, the only di¤erence is
E(IfTh;i;j >0gj; Baseh; P r emh) = 1 (1 Pr(i; j j; Baseh; P remh))T
for each draw of wh;1; :::; wh;Khand mh, …rst I compute the range of wp
hin which a given cable alternative kyields
higher utility than all other cable alternatives (this computation is similar to a standard vertical model). After
computing these ranges for each k= 2:::4, I simulate a …xed number of draws of wp
hfrom each range, and compute
the choice probability above for each draw of wp
h. The draws of wp
hfrom di¤erent ranges are weighted proportionally
to the probability of wp
hbeing in each range.
104An alternative would be to compute E(Th;i;j j; Baseh=k);i.e. viewing time conditional on subscription:
However, in this case it would be much harder to integrate out the unobservables wh; mh, because their distribution
conditional on B aseh=kis di¤erent from the unconditional distribution. Furthermore, the simulation would no
longer be unbiased.
45
For premium channels, I also compute E(Th;i;j P remh;j j);equal to
E(Th;i;j P remh;j j) =
=X
Baseh=1:::5
E(Th;i;j j; Baseh; P r emh;j = 1) Pr(P r emh;j = 1j; Baseh) Pr(Basehj)
The second outcome is E(Th;i;j1Th;i;j2j);computed for each j1; j2(j16=j2). It is equal to
E(Th;i;j1Th;i;j2j) = X
Baseh;P remh
E(Th;i;j1Th;i;j2j; Baseh; P r emh) Pr(P remhj; Baseh) Pr(Baseh=kj)
where the summation is for Baseh= 1:::5and for all possible values of P remhgiven Baseh.
E(Th;i;j1Th;i;j 2j; Baseh; P remh)can be computed fast as
E(Th;i;j1Th;i;j2j; Baseh; P r emh) = T(T1) Pr(i; j1j; Baseh; P remh) Pr(i; j2j; B aseh; P remh)
(the multiplier is T(T1) not T2because only one channel can be chosen at any given t):
The third outcome is E(Th;i1Th;i2j);where Th;i P
j=1:::J
Th;i;j denotes total viewing time for
household member i.Basehand P r emhare integrated out in the same way as for E(Th;i;j1Th;i;j2j);
and E(Th;i1Th;i2j; Baseh; P r emh)is computed as
E(Th;i1Th;i2j; Baseh; P r emh) = T2Pr(i1j; Baseh; P remh) Pr(i2j; B aseh; P remh)
where Pr(ij; Baseh; P r emh)P
j=1:::J
Pr(i; jj; B aseh; P remh)is the per-period probability of
watching any of the Jcable channels.
Direct computation of the viewing-time outcomes would require summation over all pos-
sible combinations of Basehand P remh;which is hundreds of combinations. To reduce the
computational burden to a reasonable level, I use simulation instead. Speci…cally, for each draw
of wh; mh, I generate one draw of Baseh; P remhbased on the probabilities Pr(Baseh=kj);
Pr(P remh;j j; Baseh=k);and replace the summation (which corresponds to taking expectation)
with an unbiased simulation based on this draw105.
105A direct frequency simulator would not be di¤erentiable, therefore I use a simple importance sampler to smooth
it out (i.e. I keep the draws of B aseh,P remh…xed and adjust the weights on these draws).
46
Appendix B.
Table 1. Subscriptions
4 DMAs* national**
antenna 23% 16%
basic-only 7%*** 8%
expanded basic 24%*** 34%
digital cable 26% 23%
satellite 20% 19%
* Simmons data. The 4 DMAs are Boston (MA only), Los Angeles, New York (NY only) and San Francisco.
** Data from FCC (2005a).
*** Simmons does not distinguish between basic-only and expanded-basic subscriptions (both are pooled into “analog cable”). The break-
down between basic and expanded basic uses the national proportion of basic-only subscribers, from FCC (2005a).
Table 2. Viewership among cable/satellite subscribers, channel availability on cable
Viewership among
cable/satellite subscribers
channel
availability break-
down of
analog**
channel genre* %
watched
avg time
avg non-
zero time
analog
digital
basic
exp.
basic
A&E
general entertainment 19% 0.49 2.5 96% 0% 7% 89%
ABC Family
family 12% 0.30 2.5 100%
0% 5% 95%
AMC
movies 11% 0.30 2.7 96% 0% 4% 92%
Animal Planet
education/learning 17% 0.36 2.1 73% 17% 2% 72%
BBC America general entertainment 5% 0.10 2.2 0% 72% – –
BET general entertainment 7% 0.28 3.9 90% 0% 3% 89%
Bravo general entertainment 11% 0.25 2.3 84% 10% 4% 80%
Cartoon Network family 11% 0.34 3.0 93% 0% 0% 94%
Cinemax (premium) movies 8% 0.25 3.1 – – – –
CMT general entertainment 4% 0.07 1.9 40% 31% 0% 41%
CNBC news/information 13% 0.31 2.4 99% 0% 8% 92%
CNN news/information 24% 0.56 2.3 100%
0% 5% 95%
CNN Headlines news/information 13% 0.28 2.2 90% 0% 3% 88%
Comedy Central general entertainment 18% 0.48 2.6 96% 0% 6% 91%
Court TV general entertainment 9% 0.22 2.5 85% 2% 3% 82%
Discovery education/learning 28% 0.72 2.6 98% 0% 24% 73%
Discovery Health education/learning 5% 0.12 2.4 0% 9% – –
Disney family 9% 0.25 2.9 83% 11% 1% 84%
E! general entertainment 15% 0.31 2.0 96% 0% 6% 90%
Encore (premium) movies 4% 0.10 2.6 – – – –
ESPN sports 20% 0.62 3.1 100%
0% 10% 90%
ESPN2 sports 13% 0.36 2.9 91% 1% 2% 89%
ESPN Classic sports 4% 0.07 1.9 24% 62% 0% 23%
ESPN News sports 7% 0.17 2.4 0% 73% – –
Food Network education/learning 19% 0.58 3.1 86% 7% 23% 63%
FOX News news/information 20% 0.60 3.0 91% 0% 5% 88%
FOX Sports sports 14% 0.43 3.0 87% 8% 5% 84%
Fuse general entertainment 1% 0.02 2.3 10% 48% 0% 10%
FX general entertainment 13% 0.32 2.5 86% 0% 3% 83%
GSN general entertainment 4% 0.12 2.8 23% 58% 1% 17%
Hallmark family 3% 0.08 2.5 49% 17% 4% 46%
HBO (premium) movies 26% 0.96 3.7 – – – –
History Channel education/learning 20% 0.55 2.7 91% 4% 3% 90%
HGTV education/learning 13% 0.41 3.2 62% 26% 1% 59%
IFC movies 4% 0.12 2.7 5% 70% 0% 6%
Lifetime women's programming 20% 0.70 3.5 100%
0% 8% 92%
TMC (premium) movies 8% 0.22 2.7 – – – –
MSNBC news/information 17% 0.41 2.5 92% 0% 9% 84%
MTV general entertainment 20% 0.50 2.6 100%
0% 8% 92%
Nat'l Geographic
education/learning 9% 0.21 2.4 12% 45% 4% 10%
Nick @ nite
*** family 9% 0.21 2.3 100%
0% 7% 93%
Nickelodeon
*** family 8% 0.22 2.7 100%
0% 7% 93%
Outdoor Channel sports 2% 0.04 2.2 0% 35% – –
Viewership among
cable/satellite subscribers
channel
availability break-
down of
analog**
channel genre* %
watched
avg time
avg non-
zero time
analog
digital
basic
exp.
basic
Oxygen women's programming 7% 0.15 2.2 37% 26% 0% 40%
Sci-Fi general entertainment 12% 0.35 3.0 81% 15% 0% 81%
Showtime (premium) movies 11% 0.33 2.9 – – – –
Soapnet women's programming 2% 0.08 3.7 8% 27% 0% 8%
Speed sports 4% 0.12 3.3 26% 48% 0% 24%
Spike general entertainment 12% 0.30 2.6 97% 0% 3% 95%
StarZ (premium) movies 10% 0.33 3.3 – – – –
Style general entertainment 2% 0.05 2.5 11% 54% 0% 12%
TBS general entertainment 24% 0.64 2.7 98% 0% 45% 53%
TLC education/learning 19% 0.58 3.1 98% 0% 7% 92%
TNT general entertainment 24% 0.64 2.7 100%
0% 5% 95%
Toon Disney family 5% 0.11 2.4 3% 70% 0% 4%
Travel Channel education/learning 11% 0.25 2.3 66% 17% 3% 60%
TCM movies 7% 0.21 3.1 21% 54% 1% 19%
TV Guide general entertainment 6% 0.06 1.0 74% 0% 60% 14%
TV Land family 8% 0.21 2.5 66% 28% 1% 63%
USA general entertainment 21% 0.55 2.6 100%
0% 7% 93%
VH1 general entertainment 12% 0.25 2.2 99% 0% 7% 92%
WE women's programming 6% 0.14 2.6 43% 39% 0% 39%
Weather Channel news/information 19% 0.21 1.1 95% 0% 10% 85%
WGN general entertainment 4% 0.09 2.3 19% 0% 15% 5%
Viewing data: Simmons, 4 DMAs.
Channel availability data: Television and Cable Factbook 2005, 4 DMAs, cable systems weighted by system size.
Premium channels are always offered separately from the main tiers.
* the channel genres are from DISH Network’s site, www.dishnetwork.com, with minor modifications.
** only for cable systems offering separate basic and expanded-basic packages (90.7% of all systems, weighted by system size).
*** Nickelodeon and Nick @ Nite are reported separately in the data, so I treat them as if they are two separate channels.
Table 3. Correlation of viewing time for selected channels (cable/satellite subs only)
Cartoon
Network
CNN Discovery
ESPN HBO Lifetime
MTV TBS
A&E
-0.01 0.15 0.15 0.03 0.01 0.10 -0.04 0.10
ABC Family
0.07 0.06 0.09 0.01 0.01 0.13 0.05 0.10
Animal Planet
0.10 0.06 0.24 0.03 0.01 0.07 0.01 0.04
BET 0.06 0.01 0.01 0.04 0.07 0.09 0.22 0.05
Cartoon Network - -0.02 0.07 0.00 0.08 0.01 0.07 0.04
CNN -0.02 - 0.12 0.11 0.05 0.04 -0.03 0.02
CNN Headlines -0.01 0.35 0.11 0.04 0.02 0.04 -0.02 -0.01
Comedy Central 0.10 0.04 0.09 0.06 0.07 0.02 0.19 0.09
Discovery 0.07 0.12 - 0.06 0.06 0.03 0.02 0.07
E! 0.03 0.06 0.10 0.05 0.08 0.10 0.22 0.07
ESPN 0.00 0.11 0.06 - 0.12 -0.03 0.05 0.11
ESPN2 0.03 0.08 0.06 0.67 0.13 -0.02 0.06 0.15
Food Network -0.01 0.06 0.14 0.05 0.02 0.07 0.05 0.04
FOX News -0.02 0.19 0.11 0.08 0.03 0.03 -0.03 0.00
FOX Sports -0.01 0.11 0.05 0.36 0.08 -0.01 0.02 0.07
HBO 0.08 0.05 0.06 0.12 - 0.08 0.11 0.07
History Channel 0.00 0.14 0.30 0.06 0.11 0.04 -0.03 0.07
HGTV -0.03 0.05 0.13 -0.02 0.03 0.07 -0.01 0.01
Lifetime 0.01 0.04 0.03 -0.03 0.08 - 0.05 0.16
MTV 0.07 -0.03 0.02 0.05 0.11 0.05 - 0.09
N
ickelodeon 0.24 -0.04 0.03 -0.01 0.03 0.02 0.08 0.05
Showtime 0.05 0.01 0.05 0.04 0.32 0.02 0.06 0.07
TBS 0.04 0.02 0.07 0.11 0.07 0.16 0.09 -
TLC 0.02 0.03 0.21 0.01 0.04 0.08 0.09 0.15
TNT 0.01 0.07 0.05 0.08 0.08 0.11 0.03 0.31
USA -0.01 0.03 0.03 0.06 0.06 0.14 0.03 0.27
VH1 0.02 0.02 0.03 0.04 0.10 0.05 0.34 0.10
0
10
20
30
40
50
60
70
0 20 40 60 80
#channels
price
Figure 1. Variation across cable systems in price and number of channels for the most popular
combination of packages (basic plus expanded-basic)
Table 4. The estimates
(a) ηj, ηjf
ηj ηjf ηj ηjf
channel est. s.e. est. s.e. channel est. s.e. est. s.e.
A&E -3.63 0.19 -3.00 0.09 History Channel -3.74 0.20 -2.64 0.08
ABC Family -2.41 0.12 -2.66 0.10 HGTV -3.24 0.19 -2.40 0.09
AMC -4.11 0.30 -2.95 0.11 IFC -4.03 0.32 -2.44 0.19
Animal Planet -4.64 0.27 -3.52 0.13 Lifetime -8.60 1.65 -4.26 0.63
BBC America -3.77 0.25 -2.81 0.20 TMC -3.52 0.26 -2.48 0.13
BET -4.78 0.37 -2.91 0.17 MSNBC -3.05 0.23 -2.60 0.12
Bravo -3.39 0.17 -2.70 0.09 MTV -3.62 0.19 -2.70 0.09
Cartoon Network
-2.90 0.15 -2.59 0.08 Nat'l Geographic -3.77 0.20 -2.83 0.12
Cinemax -3.82 0.26 -2.49 0.13 Nick @ nite -2.30 0.13 -2.41 0.11
CMT -3.27 0.26 -2.61 0.20 Nickelodeon -3.10 0.17 -2.52 0.10
CNBC -3.16 0.23 -2.57 0.11 Outdoor Channel -6.26 0.82 -3.74 0.58
CNN -3.47 0.20 -3.05 0.10 Oxygen -9.74 1.78 -3.98 0.49
CNN Headlines -3.46 0.25 -2.85 0.13 Sci-Fi -3.08 0.17 -2.40 0.10
Comedy Central -3.82 0.18 -2.99 0.10 Showtime -3.66 0.26 -2.45 0.13
Court TV -3.29 0.21 -2.62 0.13 Soapnet -10.13 1.89 -3.87 0.53
Discovery -3.82 0.18 -2.89 0.07 Speed -4.92 0.64 -2.70 0.21
Discovery Health
-4.25 0.27 -3.00 0.20 Spike -3.91 0.21 -2.86 0.12
Disney -2.80 0.15 -2.47 0.08 StarZ -3.50 0.28 -2.32 0.13
E! -4.25 0.22 -3.26 0.10 Style -3.60 0.27 -2.54 0.20
Encore -3.60 0.30 -2.32 0.17 TBS -3.40 0.17 -2.83 0.08
ESPN -4.61 0.60 -2.60 0.12 TLC -3.48 0.18 -2.51 0.07
ESPN2 -4.67 0.61 -2.59 0.12 TNT -3.16 0.14 -2.74 0.07
ESPN Classic -5.53 0.59 -3.32 0.25 Toon Disney -3.19 0.16 -2.49 0.11
ESPN News -4.97 0.61 -2.86 0.16 Travel Channel -3.68 0.19 -2.76 0.09
Food Network -4.01 0.21 -3.00 0.11 TCM -3.43 0.27 -2.43 0.13
FOX News -2.97 0.21 -2.56 0.10 TV Guide -3.71 0.22 -3.22 0.17
FOX Sports -4.79 0.62 -2.97 0.15 TV Land -2.26 0.16 -2.35 0.12
Fuse -4.15 0.47 -2.52 0.43 USA -3.01 0.14 -2.62 0.07
FX -3.57 0.18 -2.83 0.10 VH1 -3.85 0.21 -2.87 0.12
GSN -2.85 0.23 -2.27 0.19 WE -9.53 1.71 -3.92 0.40
Hallmark -2.41 0.17 -2.60 0.17 Weather Channel -4.14 0.21 -3.92 0.14
HBO -3.28 0.25 -2.25 0.10 WGN -3.05 0.24 -2.51 0.18
(b) preferences for channel genre – demographics
gen.
entertainment
education /
learning sports movies family news /
information women’s
programming
est. s.e. est. s.e. est. s.e. est. s.e. est. s.e. est. s.e. est. s.e.
male* -0.84
0.09 -0.86 0.10 -1.02 0.15 -0.82 0.09 -0.72 0.08 -0.86 0.09 -1.51 0.30
age* 2.65 0.60 2.92 0.64 5.04 1.55 0.87 0.66 -2.52 0.58 3.20 0.88 25.25 7.80
hispanic -0.12
0.03 -0.05 0.03 0.02 0.05 0.03 0.04 -0.21 0.04 -0.15 0.04 -0.68 0.34
black* -0.41
0.10 -0.23 0.07 -0.25 0.13 -0.41 0.12 -0.46 0.10 -0.27 0.08 0.32 0.42
dropout -0.06
0.03 -0.09 0.03 0.05 0.06 -0.02 0.03 -0.10 0.03 -0.10 0.03 -0.19 0.28
college+* -1.32
0.16 -1.54 0.18 -1.75 0.21 -1.46 0.17 -1.18 0.14 -1.64 0.19 -1.26 0.26
student 0.15 0.05 0.05 0.04 0.17 0.08 -0.02 0.06 -0.07 0.04 0.08 0.05 0.77 0.40
employed 0.01 0.02 -0.02 0.03 -0.09 0.06 -0.10 0.04 -0.05 0.02 0.01 0.03 0.11 0.23
children in hh*
-1.39
0.13 -1.34 0.13 -1.24 0.13 -1.55 0.15 -1.83 0.17 -1.20 0.12 -1.13 0.31
hh income 0.95 0.45 3.94 0.72 7.35 2.08 4.80 1.03 -0.80 0.45 0.32 0.50 12.57 5.42
* notice that demographics also enter channel preferences via the demographic-match parameters in (c) below.
(c) other parameters in channel preferences (except for UH parameters)
ψ-s demographic match* miscellaneous
est. s.e. est. s.e. est. s.e.
const -0.28 0.19 male*%male 1.73 0.19 ∆ratings** 1.23 0.22
ratings 2.09 0.72 (age – avg age)^2 -0.09 0.07 κ0*** 10.00 –
education/learning
-0.26 0.17 black*%black 2.66 0.46 κ1*** 40.00 –
sports -0.35 0.19 college*%college 5.64 0.67 ηotherDBS 0.31 0.05
movies -0.01 0.21 children*%children
3.71 0.35 ηother -6.93 0.91
family 0.18 0.17 ρ 0.07 0.04
news/information 0.14 0.24
women 0.00 0.36
* demographic match: respondent’s demographics vs average audience demographics for channel j.
** ∆ratings = DMA rating – national rating for channel j (proxies for local preferences).
*** the κ-s kept going to infinity (leading to overflow), therefore I fixed them at reasonably high values.
(d) unobserved heterogeneity in genre preferences
(instead of directly estimating Ω, I estimate a triangular matrix of coefficients on u~N(0,I7*7))
u1 u2 u3 u4 u5 u6 u7
est s.e. est s.e. est s.e. est s.e. est s.e. est s.e. est s.e.
gen. entertainment
-0.26 0.03 -0.14 0.02 -0.10 0.04 -0.22 0.03 -0.15 0.03 -0.07 0.02 -0.70 0.18
education/learning
-0.21 0.03 0.08 0.03 0.01 0.02 -0.09 0.02 -0.11 0.03 0.04 0.15
sports -0.31 0.08 -0.09 0.03 -0.10 0.03 -0.16 0.04 0.68 0.20
movies -0.25 0.04 -0.09 0.03 -0.05 0.03 -0.41 0.21
family 0.17 0.03 -0.05 0.03 0.77 0.28
news/information 0.11 0.04 1.10 0.37
women -0.42 0.47
(e) factor-analytic parameters Пj
(estimated only for the top-32 channels, 3 dimensions. I do not impose a rotation normalization because the parameters are
pinned down anyway for a finite number of simulation draws)
dimension 1 dimension 2 dimension 3
channel est. s.e. est. s.e. est. s.e.
A&E 0.06 0.04 -0.48 0.07 0.09 0.05
ABC Family -0.16 0.04 -0.19 0.04 0.05 0.03
AMC -0.11 0.07 -0.54 0.10 -0.03 0.07
Animal Planet -0.12 0.06 -0.55 0.11 0.25 0.07
BET -0.44 0.12 -0.17 0.08 0.35 0.09
Cartoon Network -0.05 0.02 -0.11 0.03 0.07 0.02
CNN -0.01 0.03 -0.21 0.05 -0.05 0.03
CNN Headlines 0.05 0.03 -0.17 0.04 -0.10 0.04
Comedy Central -0.15 0.04 -0.15 0.04 0.21 0.04
Court TV 0.12 0.04 -0.30 0.05 0.09 0.04
Discovery 0.01 0.03 -0.26 0.04 0.12 0.03
E! -0.21 0.07 -0.27 0.06 0.30 0.06
ESPN -0.24 0.07 -0.13 0.04 0.04 0.03
ESPN2 -0.25 0.07 -0.11 0.04 -0.01 0.04
Food Network -0.18 0.05 -0.20 0.04 0.24 0.05
FOX News 0.02 0.03 -0.10 0.03 0.16 0.04
FOX Sports -0.13 0.05 -0.23 0.07 0.15 0.05
dimension 1 dimension 2 dimension 3
channel est. s.e. est. s.e. est. s.e.
FX -0.09 0.03 -0.21 0.04 0.07 0.03
HBO -0.05 0.02 -0.04 0.02 0.07 0.02
History Channel 0.01 0.03 -0.18 0.03 0.09 0.03
HGTV -0.02 0.02 -0.06 0.02 0.10 0.03
Lifetime -0.44 0.17 -0.85 0.20 0.15 0.15
MTV -0.24 0.05 0.00 0.04 0.22 0.05
Sci-Fi 0.03 0.03 -0.10 0.02 -0.02 0.02
Showtime 0.00 0.03 -0.14 0.03 0.00 0.03
Spike -0.21 0.04 -0.25 0.05 0.04 0.04
TBS -0.28 0.06 -0.25 0.05 -0.14 0.04
TLC -0.08 0.03 -0.11 0.02 -0.01 0.02
TNT -0.15 0.03 -0.18 0.03 -0.04 0.03
USA -0.08 0.02 -0.08 0.02 -0.04 0.02
VH1 -0.24 0.05 -0.04 0.03 0.14 0.04
Weather Channel -0.10 0.04 -0.14 0.04 -0.02 0.03
(f) bundle choice parameters
base bundle choice coefficients on price residuals premium subscriptions
est. s.e. est. s.e. est. s.e.
ξbasic 0.34 1.82 in ξbasic ηcinemax 5.83 1.89
ξexp.basic 3.75 4.02 γbasic 1.62 0.52 ηencore 6.43 2.02
ξdigital 2.18 5.41 γexp.basic -0.51 0.31 ηhbo 5.41 1.68
ξDBS -11.84 5.20 γdigital 0.34 0.60 ηtmc 5.90 1.90
φ0 49.8 8.4 in ξexp.basic ηshowtime 5.56 1.82
φ1 2.73 0.85 γbasic 0.81 0.29 ηstarz 5.62 1.87
α0 -0.37 0.11 γexp.basic 0.11 0.14 φprem,0 0.56 0.12
αincome 0.011 0.002 γdigital -1.10 0.52 φprem,1 0.03 0.02
αw* 0.10 – in ξdigital αprem,income -0.01 0.41
σlogit* 0.22 0.54 γbasic 1.20 0.32
λhouse 2.53 0.81 γexp.basic 0.39 0.19
λownHouse -1.72 0.71 γdigital -1.81 0.80
λNY -0.89 0.86
λLA -1.62 0.84
λSF -0.23 0.76
* I normalize the variance of whp and instead estimate the variance of the logit shocks.
I use 20 simulation draws in estimation. The standard errors above do not account for the simulation variance and variance of
the first-stage parameters (distribution of locations for each household and price residuals). The sample is stratified by the
X-s, so I do not use sample weights in estimation (Wooldridge [2001] shows that unweighted estimators are more efficient in
this case).
0
0.2
0.4
0.6
0 0.2 0.4 0.6
actual
predicted
mean time
0
0.05
0.1
0.15
0.2
0.25
0 0.05 0.1 0.15 0.2 0.25
actual
predicted
proportion of non-zero time
Figure 2. Predicted and actual mean time and % non-zero time for each channel
-0.2
0
0.2
0.4
0.6
0.8
1
-0.2 0 0.2 0.4 0.6 0.8 1
actual
predicted
all pairs of the top-32 channels
-0.2
0
0.2
0.4
0.6
0.8
1
-0.2 0 0.2 0.4 0.6 0.8 1
act ual
all pairs of the 64 channels
Figure 3. Predicted and actual covariances of viewing time for each pair of channels
0
2
4
6
8
02468
actual
predicted
top-32 channels
0
2
4
6
8
0 2 4 6 8
actual
predicted
all channels
Figure 4. Predicted and actual covariances of viewing time between each channel and the rest of
channels combined
Table 5a. Descriptive statistics for the “themed tiers”
general
entertainment education /
learning sports movies (non-
premium) family news /
information women's
programming
average time*
6.0 3.7 1.8 0.6 1.7 2.4 1.0
% of total** 35% 21% 10% 4% 10% 14% 6%
license fees 3.81 1.1 4.33 0.63 1.63 1.13 0.34
% of total** 29% 8% 33% 5% 13% 9% 3%
#channels 20 8 6 3 8 6 3
list of
channels
A&E
, BBC
A
merica, BET,
Bravo, CMT,
Comedy Central,
Court TV, E!, F
use,
FX, GSN, MTV,
SCI-FI,
Spike TV, Style,
TBS, TNT, TV
Guide, USA, VH1
A
nimal Planet,
Discovery,
Food Network,
HGTV, History
Channel,
N
ational
Geographic,
TLC,
Travel Channel
ESPN,
ESPN Classic,
ESPN News,
ESPN2,
FOX Sports,
Speed
Channel
AMC
,
IFC,
TCM
ABC F
amily,
Cartoon
N
etwork,
Disney,
Hallmark,
N
ickelodeon/
N
ick at Night,
Toon Disney,
TV Land
CNBC,
CNN,
CNN
Headlines,
FOX News,
MSNBC,
Weather
Channel
Lifetime,
Oxygen,
WE
* hours per week per person, among cable/satellite subscribers
** % of the total among the 54 cable channels in the “representative bundle”
Table 5b. Descriptive statistics for the mini-tiers by owner
Disney Time Warner Fox NBC Universal
Viacom other
average time*
3.6 2.9 1.7 1.9 2.6 4.4
% of total** 21% 17% 10% 11% 15% 26%
license fees 4.41 2.02 2.26 1.11 1.35 1.82
% of total** 34% 16% 17% 9% 10% 14%
# channels 10 7 6 5 10 16
list of channels
A&E,
ABC Family,
Disney, ESPN,
ESPN Classic,
ESPN News,
ESPN2,
History
Channel,
Lifetime,
Toon Disney
Cartoon
Network,
CNN,
CNN Headlines,
Court TV, TBS,
TCM, TNT
FOX News,
FOX Sports,
FX,
National
Geographic,
Speed Channel,
TV Guide
Bravo, CNBC,
MSNBC,
Sci-Fi, USA
BBC America,
BET, CMT,
Comedy
Central,
MTV,
Nickelodeon/
Nick at Night,
Spike TV,
TV Land,
VH1
AMC, Animal
Planet,
Discovery, E!,
Food Network,
Fuse, GSN,
Hallmark, GTV
,
IFC, Oxygen,
Style, TLC,
Travel Channel,
WE, Weather
Channel
* hours per week per person, among cable/satellite subscribers
** % of the total among the 54 cable channels in the “representative bundle”
Table 6. Bundling outcomes for various cost scenarios
(a)
original
license fees
(b)
Pbasic=15,
orig. lic. fees
(c)
lump-sum
license fees
(d)
revenue-
sharing
prices
basic 23.13 15.00 28.66 29.02
full bundle 46.64 46.46 42.30 42.13
market shares
antenna 24.4% 19.6% 25.0% 25.0%
satellite 21.3% 20.6% 17.0% 16.8%
basic only 7.0% 13.6% 4.2% 4.1%
full bundle 47.3% 46.3% 53.8% 54.1%
profits (w/o subtracting fixed costs), $ per household in population
cable operator 18.80 18.61 19.27 19.20
networks 17.8 17.50 17.46 17.52
satellite 5.46 5.29 4.31 4.27
welfare change relative to (a), $ per household in population
∆ consumer surplus
– 0.89 1.89 1.97
∆ total welfare – 0.23 0.86 0.89
I assume that satellite does not react to changes in cable prices (in all cases, it keeps offering DirecTV Total Choice with locals at $40), and
the license-fees arrangement for satellite does not change.
Cable operator’s marginal costs are $3/sub (franchise fees plus fees to broadcast networks) plus license fees to the cable networks. Besides
subscription fees, cable operator gets revenues from its share of advertising time (which it takes into account when computing optimal
prices).
Networks’ revenues are from advertising and license fees, the marginal costs per subscriber are zero.
For satellite, I assume additional equipment costs of $5/sub per month (satellite dish+receiver).
In computing profits, I do not subtract fixed costs, which are likely to be substantial for all the industry participants.
Table 7a. “Themed tiers” for various cost scenarios
(a)
original
license fees
(b)
Pbasic=15,
orig. fees
(c)
lump-sum
license fees
(d)
revenue-
sharing
(e)
proportional
increase
welfare change relative to original bundling (column (a) in table 6)
∆ consumer surplus 0.35 1.62 1.63 1.90 -2.43
∆ total welfare 0.02 -0.33 0.85 0.97 -2.56
welfare change relative to parallel bundling (same column in table 6)
∆ consumer surplus 0.35 0.73 -0.26 -0.07 –
∆ total welfare 0.02 -0.56 -0.01 0.08 –
prices
basic 29.01 15.00 31.17 30.12 27.89
general entertainment 6.97 12.46 4.91 4.97 7.85
education/learning 2.04 6.58 2.33 3.47 1.17
sports 9.27 11.67 3.44 3.50 37.01
movies (non-premium)
3.47 3.85 2.91 2.37 9.38
family 2.81 8.06 0.97 0.94 3.82
news/information 3.38 7.65 3.04 3.37 3.20
women’s programming
0.20 1.51 0.56 0.01 0.05
market shares
antenna 23.7% 16.1% 24.0% 23.6% 23.4%
satellite* 18.9% 18.2% 16.6% 16.3% 22.1%
basic 3.1% 8.4% 2.7% 2.7% 3.4%
basic+tiers 54.3% 57.4% 56.7% 57.4% 51.1%
tier subscriptions as % of all cable subscribers
general entertainment 70% 55% 74% 74% 68%
education/learning 66% 51% 66% 64% 67%
sports 27% 21% 36% 36% 11%
movies (non-premium)
26% 22% 28% 29% 15%
family 55% 40% 60% 60% 52%
news/information 60% 44% 62% 60% 60%
women’s programming
43% 33% 42% 47% 43%
avg # tiers/sub 3.5 2.7 3.7 3.7 3.2
profits ($ per household in population)
cable operator 21.75 20.93 19.61 19.41 20.13
cable networks 15.06 14.36 17.39 17.54 15.95
satellite* 4.92 4.82 4.28 4.19 5.85
* notice that satellite is assumed to not react to cable unbundling, for reasons discussed in
section 7.1.
Table 7b. “Themed tiers” – outcomes for the networks
(% change relative to original bundling, column (a) of table 6)
(a)
original
license fees
(b)
Pbasic=15,
orig. fees
(c)
lump-sum
license fees
(d)
revenue-
sharing
(e)
proportional
increase
% change in viewership
among cable subs 1% -4% 8% 9% -11%
total cable+satellite 0% -2% 2% 2% -4%
% change in the number of subscribers
among cable subs -40% -47% -34% -33% -48%
total cable+satellite -31% -37% -30% -30% -32%
% change in license-fee revenues
among cable subs -40% -47% 0% 3% -27%
total cable+satellite -31% -37% -7% -5% -17%
% change in total revenues (advertising + license fees)
among cable subs -18% -24% 4% 6% -19%
total cable+satellite -15% -19% -2% -1% -10%
% change in viewership (among cable subs), by tier
general entertainment 1% -5% 8% 9% -7%
education/learning 3% -1% 8% 7% -4%
sports -19% -24% 2% 2% -67%
movies (non-premium) -12% -13% -4% 0% -46%
family 10% 4% 17% 18% 3%
news/information 3% -4% 8% 8% -5%
women’s programming 5% 4% 9% 11% -1%
% change in license-fee revenues (among cable subs), by tier
general entertainment -15% -24% 0% 3% -8%
education/learning -20% -29% 0% 3% -4%
sports -68% -70% 0% 3% -60%
movies (non-premium) -69% -69% 0% 3% -45%
family -33% -45% 0% 3% -9%
news/information -27% -38% 0% 3% -6%
women’s programming -48% -54% 0% 3% -5%
Table 8a. Mini-tiers by owner for various cost scenarios
(a)
original
license fees
(b)
Pbasic=15,
orig. fees
(c)
lump-sum
license fees
(d)
revenue-
sharing
(e)
proportional
increase
welfare change relative to original bundling (column (a) in table 6)
∆ consumer surplus
0.31 1.22 1.99 2.04 -0.78
∆ total welfare -0.03 -0.08 1.09 1.10 -0.94
welfare change relative to parallel bundling (same column in table 6)
∆ consumer surplus
0.31 0.34 0.10 0.08 –
∆ total welfare -0.03 -0.30 0.22 0.21 –
prices
basic 23.48 15.00 28.37 29.00 23.00
Disney 9.28 11.11 4.43 3.85 10.77
Time Warner 4.09 5.98 3.28 3.06 3.34
Fox 6.23 6.42 2.79 2.84 11.38
NBC Universal 3.29 4.76 3.32 3.11 3.22
Viacom 2.26 4.97 0.70 0.75 2.75
other 3.36 6.44 2.37 2.33 3.04
market shares
antenna 21.9% 16.5% 23.4% 23.6% 21.6%
satellite* 19.5% 18.8% 16.4% 16.3% 20.9%
basic 4.5% 8.3% 3.0% 2.9% 4.6%
basic+tiers 54.1% 56.4% 57.2% 57.2% 52.8%
tier subscriptions as % of all cable subscribers
Disney 65% 55% 78% 80% 60%
Time Warner 72% 63% 76% 77% 74%
Fox 46% 42% 61% 61% 31%
NBC Universal 60% 52% 61% 62% 60%
Viacom 64% 53% 71% 70% 62%
other 75% 63% 80% 80% 75%
avg # tiers/sub 3.8 3.3 4.3 4.3 3.6
profits ($ per household in population)
cable operator 20.41 20.02 19.58 19.40 19.21
cable networks 16.21 15.78 17.37 17.54 17.19
satellite* 5.10 4.97 4.21 4.17 5.50
* notice that satellite is assumed to not react to cable unbundling, for reasons discussed in
section 7.1.
Table 8b. Mini-tiers by owner – outcomes for the networks
(% change relative to original bundling, column (a) of table 6)
(a)
original
license fees
(b)
Pbasic=15,
orig. fees
(c)
lump-sum
license fees
(d)
revenue-
sharing
(e)
proportional
increase
% change in viewership
among cable subs -2% -4% 9% 10% -7%
total cable+satellite
-1% -2% 2% 2% -3%
% change in the number of subscribers
among cable subs -21% -25% -9% -9% -27%
total cable+satellite
-17% -21% -14% -13% -19%
% change in license-fee revenues
among cable subs -21% -25% 0% 3% -5%
total cable+satellite
-17% -21% -7% -5% -4%
% change in total revenues (advertising + license fees)
among cable subs -11% -14% 5% 6% -6%
total cable+satellite
-9% -11% -2% -1% -3%
% change in viewership (among cable subs), by tier
Disney -7% -10% 9% 10% -13%
Time Warner 1% 0% 10% 10% 0%
Fox -16% -15% 5% 5% -41%
NBC Universal 0% -2% 6% 7% -3%
Viacom 6% 4% 14% 14% 3%
other 1% -1% 9% 10% -2%
% change in license-fee revenues (among cable subs), by tier
Disney -20% -24% 0% 3% -4%
Time Warner -10% -14% 0% 3% 5%
Fox -42% -42% 0% 3% -30%
NBC Universal -26% -29% 0% 3% 3%
Viacom -21% -27% 0% 3% 1%
other -7% -14% 0% 3% 3%
