FEDERAL COMMUNICATIONS COMMISSION

MEDIA BUREAU STAFF RESEARCH PAPER

 Empirical Aspects of

Advertiser Preferences and

Program Content of 

Network Television

By Keith S. Brown and Roberto J. Cavazos

December 2003

2003-

1
The Media Bureau Staff Research Paper Series consists of reports 

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. 
2

Empirical Aspects of Advertiser Preferences and Program 

Content of Network Television

KEITH S. BROWN*

FEDERAL COMMUNICATIONS COMMISSION

ROBERTO J. CAVAZOS**

FLORIDA INTERNATIONAL UNIVERSITY

Abstract

Economists have long debated how advertising should be treated in a rational-choice 

framework. Using unique data from the broadcast networks’ 1995 Fall Season, we find that 

sitcoms attract premia from advertisers, while news and police programs get discounted. We 

interpret our findings according to two important theoretical treatments of advertising. 

Possibly because the broadcast networks forgo advertising-unfriendly program content, the 

cable channel HBO responds with a deliberate counter-programming niche strategy, 

explicitly airing programming with “darker” and “more difficult” advertising-unfriendly 

content. 

 

*Senior Economist, Media Bureau. The views expressed are those of the author and do not necessarily 

reflect the views of the Media Bureau, the Commission, or other members of the Commission’s staff.

** Assistant Professor, Department of Public Administration. The views expressed are those of the author 

and do not necessarily reflect the views of other faculty, staff, administrators, or trustees of Florida 

International University. 

The authors thank Peter Alexander, Jerry Duvall, Kenneth Ferree, Daniel Shiman, and Tracy Waldon for 

valuable and helpful comments, and Vanessa Lemme for valuable assistance. All errors are our own.
3

Table of Contents

I. Introduction..............................................................................5

II. Literature Review and Our Contribution.........................................6

III. Two Models of Advertising..........................................................7

IV. Data and Descriptive Statistics......................................................9

V. The Variables and the Model.................................................................9

VI. Estimating the Advertising Premia................................................17

VII. HBO’s Programming Strategy.......................................................18

VIII. Strategic Approaches...................................................................19

IX. Conclusion.................................................................................19
4

“The networks have essentially given up on viewers with functional IQ’s.”

Dean Valentine, President of UPN

I. Introduction

Broadcast television represents a unique market where a public good is provided by 

privately-owned, profit-driven firms1. Broadcast networks supply the public good, 

receiving no revenues directly from viewers. The networks obtain revenues by 

combining this good with advertising, which viewers may watch in order to receive the 

good2. The networks then sell commercial time to advertisers. This unique disconnect 

between consumers and purchasers of broadcast television services creates many 

thoroughly explored market imperfections. In this paper, we explore an as-yet 

empirically unexplored market imperfection in media markets, namely, one that arises 

out of advertisers’ preferences for programming. If advertisers prefer certain types of 

programming, then many viewers may not receive their preferred programming from 

advertiser-supported media3. Depending on the advertisers’ welfare gain and the 

viewers’ welfare loss, this may lead to a suboptimal pattern of programming (Anderson 

and Coate, 2000). This unique market distortion has important public policy 

implications for two reasons: 

1. The advertiser-supported broadcast television market is a dominant source of 

mass entertainment and advertising in the U.S. In 2002, there was over $41 

billion in broadcast television advertising revenue in the US alone. 

2. The broadcast television market represents the largest private provision of a 

public good in the modern United States.

Using a unique data set with advertising prices and the number of viewers during 

the 1995 Fall Television Season, we examine the effect of program content on advertising 

prices. We find that, even when adjusting for audience size and demographic 

composition, advertisers pay a premium for spot advertising on sitcoms, and pay a 

discount for advertising on “darker” programming like news magazines and police 

 

1 Here, we define a public good as a good that is non-excludable and non-rival in consumption.

2 From a public economics perspective, this may be seen as one solution to the cost recovery 

problem that arises in a natural monopoly setting, i.e., where socially efficient marginal cost pricing fails to 

cover the costs of providing the good. Anderson and Coate (2000) develop a theory of broadcasting partly 

from this perspective.

3 This likely occurs if one type of programming has a greater influence on a viewer’s likelihood of 

purchasing advertised products than another type of programming. An advertiser may be more willing to 

pay for commercial spots on less popular programming if the viewers watching less popular programming 

respond more to advertising than the viewers of more popular programming. In other words, viewers may 

prefer programming that maximizes utility net of the nuisance cost of advertising, while advertisers prefer 

programming that maximizes viewer propensity to buy the advertised product.
5

dramas. As a result, the television broadcast networks air a disproportionately high 

amount of sitcoms. This finding can be interpreted using two different theoretical 

models of advertising, namely, Becker and Murphy (1993) and Anderson and Coate 

(2000).

Looking further, we find that HBO’s business strategy may indicate that many 

viewers may not prefer broadcasters’ programming bias towards sitcoms. We then tie 

this finding into recent business strategy literature.

II. Literature Review and Our Contribution

The treatment of advertising in rational-choice theory has been a puzzle for 

economists. Two important treatments of advertising include Becker and Murphy (1993) 

and Anderson and Coate (2000). Becker and Murphy treat advertising as an argument 

in a consumer’s meta-utility function, where advertising is simply a complement to 

goods being advertised. Anderson and Coate treat advertising as simple information. A 

viewer does not know about a product until they see an advertisement for the product. 

Upon seeing an advertisement, a viewer has a willingness to pay a certain price for the 

advertised good with a certain positive probability. Anderson and Coate also examine 

the advertiser-broadcaster side of the market, explicitly solving for the price that 

advertisers pay for commercial time. Therefore, we can explicitly solve for the effect of 

program content on a viewer’s response to advertising in Anderson and Coate’s model. 

We can relate our findings, however, to both theoretical frameworks.

Anderson and Coate (2000), quoting Sunstein (1999), note the possibility that 

advertisers may directly distort network-programming choices because “[a]dvertisers 

want programming that will put viewers in a receptive mood, and hence not be too 

`depressing’”.

Researchers have explored many empirical aspects of media markets. Seldon and 

Jung (1993) and Silk, Klein, and Berndt (2002) study the substitutability of different 

media among advertisers, and Shachar and Emerson (2000) estimate audience 

preferences over program characteristics. Researchers have explored many market 

imperfections arising in advertiser-supported media markets. Networks may overduplicate programs (Steiner, 1952; Anderson and Coate, 2000); ignore smaller groups of 

viewers with intense preferences (Spence and Owen, 1977); air too little or too much 

advertising (Anderson and Coate ; Gabszewicz, Laussel, and Sonnac, 1999); or underserve viewers that aren’t as desirable to advertisers (Brown and Cavazos, 2002). Under 

some conditions, competition may even produce lower surplus than monopoly 

provision (Steiner; Anderson and Coate). In this paper, we address a heretofore 

empirically unexplored distortion in programming. Advertisers and viewers may prefer 

different programming, and the market-provided programming may not maximize 

social surplus. We empirically model the price of advertising on network television to 

reveal the programs that advertisers favor.

Models of network revenue are not new. Fisher, McGowan, and Evans (1980) first 

explored the audience-revenue relationships for local television affiliates. More recently, 
6

Goettler (1999) uses the audience-revenue relationship to evaluate network program 

placement strategies. Goettler and Schahar (2000) contend that networks do not place 

programs optimally. Specifically, networks air the same type of programming at similar 

points in time, and every network could increase their ratings if they would counterprogram (i.e., place opposing types of programming at similar points in time). 

Networks fail to engage in the optimum strategy because they fail to classify programs 

finely enough and employ boundedly-rational rules of thumb in program placement 

(such as never placing sitcoms during the final hour of prime-time). Goettler (1999) 

finds that Goettler and Schahar’s (2000) ratings-maximizing strategy also maximizes 

revenues. 

Following Fisher, et al. (1980) and Goettler (1999), we also estimate the relationship 

between network revenues and audience demographics. However, we add two major 

contributions to this line of research. First, as in Brown and Cavazos (2002), we include 

audience income in our specification. Past researchers used data sources that do not 

provide data on audience income. Our use of the Simmons Marketing Database allows 

us to include audience income in our model specification. In addition, we also include 

program content characteristics in our revenue specification, which allows us to isolate 

the value of different program content to advertisers. 

We can then estimate the effect of different program characteristics on the 

advertising price. The direct advertising premia attracted by different types of 

programming tells us which programming may be over-produced and which 

programming may be under-produced relative to the social optimum. We interpret 

these findings in light of two different theories of advertising (Becker and Murphy, and 

Anderson and Coate) and explicitly derive the effect of program content on a 

consumer’s desire for the product in the Anderson and Coate model. 

Once we estimate program characteristics’ effect on advertising revenues, we then 

provide casual but convincing empirical evidence that the resulting programming 

patterns are not those favored by viewers. This evidence comes from a New Yorker

article about HBO and their then-newest series, Six Feet Under. The article discusses the 

creative process at HBO, and demonstrates that HBO engages in a very deliberate 

counter-programming strategy against over-the-air networks. HBO deliberately 

employs darker themes, more conflict, and more complex story lines to differentiate 

themselves from network television. Since HBO is a pay channel, this suggests that 

broadcast networks systemically under-serve large groups of viewers, who are willing 

to pay directly for different programming. 

III. Two Models of Advertising

Becker and Murphy (1993) specify a model where advertising is a complement to the 

advertised good. By including advertising in the consumer’s utility function as a 

complement to the advertised product, Becker and Murphy model the consumer as 

choosing how much advertising to consume based on the prices charged for advertising. 

An advertiser may even compensate the consumer (i.e., charge negative prices) for 
7

consuming advertising, as in the case of broadcasting, where advertisers compensate 

viewers for advertising with free programming. 

In the context of Becker and Murphy, our findings indicate that the complementary 

relationship between advertising and the advertised good varies across program content 

types. A viewer may have a greater demand for the advertised product when they see 

the product’s advertising on a certain type of program. In this case, advertising on one 

type of program (such as a sitcom) may have a stronger cross-elasticity4 with the 

advertised product than advertising on another type of program (such as a news 

program). In this case, the advertiser has different demands for advertising space on 

different types of programming. Viewer preferences for programming do not 

necessarily correlate, and certainly do not perfectly correlate, with cross-elasticities 

between advertising and the goods being advertised. Therefore, the equilibrium pattern 

of programming in this advertiser-supported market will not maximize viewer welfare. 

Essentially, advertisers’ willingness to compensate viewers with programming depends 

partly on the cross-elasticities between the advertising and the advertised products, 

which distorts programming content away from content types with low cross-elasticities 

between advertising and advertised products, even if viewers have an intense 

preference for this type of programming. 

Using Becker and Murphy’s conceptual framework, differences in advertising prices 

across program content are driven by differences in cross-elasticities between 

advertising and advertised products across program content. However, we cannot 

directly infer the relative cross-elasticities between advertising and advertised goods 

across program content types from changes in the price of advertising. This is because 

the advertiser’s demand for advertising space is determined by the marginal profits in 

the product market generated by advertising, which is jointly determined by the crosselasticity between advertising and advertised goods along with the demand and cost 

conditions in the final product market. Therefore, without knowing these final demand 

and cost conditions, we cannot estimate these cross-elasticities for the marginal viewer. 

However, if we assume the initial similarity of goods advertised on different types of 

programs, then we can infer that program content types that receive advertising premia 

have a higher cross-elasticity between advertising and the advertised goods, at least for 

the marginal viewer.

Anderson and Coate (2000) provide another important treatment of advertisersupported media. In their treatment, there exist two possible channels, each of which 

may carry one of two types of programming. Firms develop products at zero cost, and 

use advertising to inform the consumers about the existence of a product. A given 

consumer either values the product at 0 or at 

  0

. The desirability of the product is 

given by 

 [0, ]

where 

 1

, which represents the proportion of consumers who 

desire the product at price 



. Therefore, advertisers are willing to pay their expected 

profit gained from an advertisement, 



, to reach a viewer. There are a total of 

m

firms 

seeking to advertise, and broadcasters choose to air 

a

advertisements. Therefore, the 

(approximate) inverse demand curve for advertising, given a per-viewer advertising 

price of 

p

as a function of the number of advertisements a, is

 

4 The word “cross-elasticity” in this case refers to the cross-quantity relationship between a viewer’s 

consumption of advertising and the viewer’s demand for the advertised good. 
8

(1)

To simplify our estimation, we assume that 

a m  0 (which closely approximates 

reality, when we consider the extremely large number of firms that advertise on network 

television). This generates a per-viewer advertising price of

p 

. Adjusting for the 

number of viewers, viewer demographic and program content characteristics raise 

(lower) the price of advertising by raising (lowering) the probability that viewers are 

willing to buy the advertised good at the advertised price of



. If we represent these 

content and demographic characteristics as a vector 

1

( ,..., )   n

, then the per-viewer price 

of advertising is

1

( ,..., ),

n

p      (2)

and we estimate each value for 

1

( ,..., )   n

.

IV. Data and Descriptive Statistics

We employ data from several sources, including Broadcasting and Cable, Advertising 

Age, the Simmons Marketing Database, and The Complete Directory of Prime-Time Network 

and Cable TV Shows. We could not obtain data for the actual advertising prices. 

However, the July 31st, 1995 issue of Broadcasting and Cable provided forecasts of 

program shares for the 1995-96 season, while the August 14th, 1995 issue of Advertising 

Age provided forecasts of the expected price for a 30 second commercial spot for the 

1995-96 season. The Simmons Marketing Database provided survey information on the 

actual ex-post number of viewers for each program, as well as their demographic 

composition, including age, race, gender, and income. The Complete Directory of PrimeTime Network and Cable TV Shows provided program descriptions, which allowed us to 

classify programs based on their characteristics, including cast demographics and 

program content. The sample consists of 71 programs from the “Big Three” networks, 

namely, ABC, CBS, and NBC. 

V. The Variables and the Model

We explain the expected price of a 30-second commercial spot on network television 

as a function of the expected viewer share, viewer demographics, and program 

characteristics. We also include the ex-post total number of viewers from the Simmons 

Marketing Database for a few important reasons. First, the FOX network airs no 

programming in the final hour of prime time, which could lead to higher shares during 

that last hour for the networks in our sample, even with fewer viewers. Including the 

ex-post number of viewers from the Simmons survey adjusts for that. In addition, 

Goettler and Schahar argue that networks follow imperfect placement strategies, airing 

similar programs at the same time even when they could gain viewers by airing 

different types of programs. This failure is at its worst during the final hour of prime 

p a a m ( ) [1 ]    
9

time, when no networks air any type of sitcom. Once again, if networks fail to maximize 

the number of viewers, then this leads to fewer overall viewers. If this problem is 

systemically worse at certain time periods, then shares during these time periods could 

be inflated. Each network may be drawing a large share of a smaller number of viewers. 

In addition, because no sitcoms air during this hour of excessive duplication, the 

coefficient on sitcoms could be biased upward. Including the ex-post total number of 

viewers adjusts for this.

We follow Goettler in examining the question of gender concentration. However, we 

create a new variable, GenConc, which is equal to the square of (the percentage of female 

viewers minus the percentage of male viewers).

Following Fisher, et al (1980) and Goettler (1999), we wish to see if there’s a convex 

relationship between the share of viewers and the price of network advertising. Both 

Fisher and Goettler argue that the price of advertising is a convex function of the 

number of viewers, because programs may duplicate viewers. For example, two 

programs reaching 20 million viewers each will not reach a total of 40 million viewers, 

because some viewers watch both programs. Because viewers may watch more than 

one program, an advertisement on two programs with x viewers reaches fewer viewers 

than an advertisement on one program with 2x viewers. Therefore, advertising time on 

a single program reaching 2x viewers commands a higher price than the combined 

prices of advertising time on two programs each reaching x viewers. Going back to 

equation (2), which we derived from Anderson and Coate, and modeling a world in 

which viewers could each watch only one program, the total advertising price 

P

for the 

number of viewers 

n

would be 

1

( ... ) P n        n

. In a world in which viewers 

may watch more than one program and advertisers may advertise on more than one 

program, then the total advertising price 

P

for a number of viewers 

n

would be 

( ... ) 1 n

b P  n     

, where 

b

reflects the likelihood of viewer duplication across 

programs5. A specification with a log relationship between price and viewer share 

provides a simple functional form that allows us to assess whether price and share 

follow a linear, concave, or convex relationship by allowing us to estimate the value of 

b

. However, such a specification should have a linear relationship between the 

program and demographic variables and the advertising price, because the model 

assumes that the program and demographic characteristics act as multiplicative 

constants, raising the advertising price by a given percentage regardless of the number 

of viewers. Therefore, we assume the following functional form: 

( )

14 ( )13 ( )12 ( )11 ( )10 ( )9

( )

8

( )

7

( )

6

( )

5

( )

4

(ln )

3

(ln )

1 2

( )

B Sitcom B news B Police B genx B ethnic B torc B sports B late

L n Adprice B B share B total B PerUp B PerYoung B GenConc

       

     

Table 1 defines the explanatory and summary variables specified in the model, while 

Table 2 lists the means and conditional means for expected ad price, expected share, and 

selected demographic and content characteristic variables. 

 

5 b  ln (ln( )) z y x

, where z represents the total number of different programs, each reaching 

x

viewers, 

that are required to reach 

y  x

viewers. 
10

Table 1

Definition of Explanatory Variables

Percentage Upper 

Income

Continuous variable which is the percentage of a program’s viewers that have household 

incomes greater than $50,000. (Advertisers may prefer higher-income viewers. A viewer 

is considered upper-income if they have a household income above $50,000 a year).

Percentage 18-34 Continuous variable which measures the percentage of a program’s viewers that are aged 

18-34. (Advertisers may prefer younger viewers).

Gender Concentration A variable that equals (percentage female-percentage male)2. (Advertisers may prefer a 

group of similar viewers so they can target their advertising. We expect a premium for 

viewers concentrated within one gender (Goettler)).

Sitcom Do advertisers prefer sitcoms? Sitcom = 1 when a program is a sitcom and 0 otherwise.

News Do advertisers prefer news magazines? News = 1 when a program is a news magazine 

and a 0 otherwise.

Police Do advertisers prefer police dramas? Police = 1 when a program is a police drama and a 0 

otherwise.

Genx Do advertisers prefer programs with cast members aged 18-34? Genx = 1 when a 

program has a cast whose members are 18-34 and 0 otherwise.

Sports Do advertisers prefer sporting events? Sports = 1 when a program is a sporting event and 

0 otherwise.

Ethnic Do advertisers prefer programs with minority casts? Ethnic = 1 when a program has a 

minority cast and 0 otherwise.

Teens or children Do advertisers prefer programs with teens or children in them? Teens or children = 1 

when a program has teens or children in the cast and 0 otherwise.

Late Do advertisers prefer programs that begin during the last hour of primetime? Late = 1 

when a program begins during the final hour of primetime. 
11

Table 2

Descriptive Statistics – Means and Conditional Means

Selected Characteristic 

Variables

Expected 

Ad Price

Percentage Upper > .35 245,571.4

Percentage Upper < .35 140,972.2

Percentage 18-34 > .4 214,166.7

Percentage 18-34 < .3 148,333.3

Gender Concentration > .12 233,333.3

Gender Concentration < .12 188.769.2

Sitcom 225,000.0

Police 153,000.0

News 151,428.6

Genx 245,500.0

Sports 385,000.0

Ethnic 165,000.0

Teens or Children 178,863.6

Overall 192,353.2

A two-sided Box-Cox test, with price as the dependent variable and with share and 

total as the sole transformed independent variables, gives a test statistic of 

  .03

with a z-score of 0.16 and a 95% confidence interval of - 0.45 to 0.38. When we use a 

likelihood ratio test to try to reject the null hypothesis of 

  0

(which would be our 

specification), we get a Chi-square value of 0.03 and only a 13% probability that 

  0

would be the incorrect specification. Therefore, we can safely employ our specification 

with advertising price as the dependent variable and share and total as the sole 

transformed independent variables. The results of this specification can be found on 

Table 3. We estimate all regressions with White-corrected standard errors6.

The demographic results bear important similarities to Goettler’s results. The 

coefficient on log (share) is significantly greater than one, implying a convex relationship 

between price and share. Our own contribution to the demographic data reveals that 

advertisers strongly prefer upper-income viewers. The coefficient on GenConc, equal to 

 

6 Brown and Cavazos (2002) employ a Hausman test showing that a specification with expected price as 

the dependent variable and expected share as an independent variable does not suffer from endogeneity.
12

.008, is positive and significant, indicating that advertisers strongly prefer concentrated 

gender groups7.

Examining our vector of content characteristics, we find that sitcoms and programs 

with characters aged 18-34 receive premia from advertisers, while news programs, 

Table 3

Determinants of Expected Advertising Price: The Full Model Regression Coefficients 

and Goodness of Fit Statistics

(t-statistics in parentheses)

Variable Estimated Coefficient t Statistic

Log (Expected Share) 1.39*** (9.38)

Log (Total Viewers) 0.05 (1.47)

Percentage Upper-Income 0.01** (2.55)

Percentage 18-34 -0.00 (0.02)

Gender Concentration 0.01* (1.88)

Sitcom 0.20** (2.52)

News -0.26*** (3.81)

Police -0.26*** (3.10)

Genx 0.16** (2.06)

Ethnic 0.08 (0.46)

Teens or Children -0.12* (1.76)

Sports 0.09 (1.01)

Late 0.13* (1.97)

Constant 7.01*** (22.27)

Observations 71

Adjusted R-Squared .83

F-Statistic 27.74***

*** - significant at 99% confidence level, ** - significant at 95% confidence level, * - significant at 

90% confidence level

 

7 When we examined specifications using both percentage male and percentage female, we obtained very 

similar and symmetric results. Therefore, another gender concentration variable which equals (percentage 

male – percentage female)

2

would add no explanatory power to the model.
13

police dramas, and programs with teens or children as part of the cast get discounted. 

Programs with minority casts receive no significantly significant premia or discounts, 

which confirms the finding of Brown and Cavazos (2002) that minority programs do not 

appear to suffer from any advertiser bias. 

We can interpret our results using Becker and Murphy’s framework. More affluent 

viewers and more concentrated gender groups may have higher demands for the 

advertised good, creating a higher cross-elasticity between advertising and the 

advertised good. Sitcoms, programs with casts aged 18-34, and programs airing during 

the final hour of prime-time raise the cross-elasticity between advertising and the 

advertised goods, while news magazines, police dramas, and programs featuring teens 

or children tend to lower the cross-elasticity. Again, we cannot estimate the extent to 

which these content characteristics change the cross-elasticity between advertising and 

the advertised product because we do not know the advertised product’s cost and 

demand functions. 

We also interpret our results using Anderson and Coate. Recall the model of 

advertising price that we derived using Equation (2), namely, 

1

( ,..., ),

n

p     

Each 



corresponds to a demographic and program characteristic variable and is equal 

to 

e

raised to that variable coefficient’s value. Table 4 lists the values of 



for the 

dummy characteristics with statistically significant coefficients8. At the mean value of 

upper-income viewers (35%), a 1% increase in the percentage of upper income viewers 

increases the advertising price by 2%. At the mean value of gender composition (3.9), a 

1% increase in the larger gender group and a 1% fall in the smaller gender group 

increase the advertising price by 0.9%. 

Table 4

Derived Values of 

 's

(For Statistically Significant Program Characteristics Only)

_____________________________________________________

1.137

0.889

1.172

0.774

0.775

1.219

1 4

1 2

1 0

9

8

7

 

 

 

 

 

 

B

late

B

torc

B

genx

B

Police

B

news

B

Sitcom

e

e

e

e

e

e













_______________________________________________________

 

8

In the case of our continuous demographic variables, we have to interpret the derivatives with respect to a 

change in the variable’s value. Given our specification, this derivative is 

i i B x

i B e .
14

A sitcom commands 22% more advertising dollars per spot, while news magazines 

and police dramas get discounted at 22% and 23% per spot. Therefore, even after 

adjusting for the number and demographics of viewers, sitcoms receive over 40% more 

advertising dollars per 30-second commercial spot than news programs and police 

dramas. Casts aged 18-34 raises a program’s advertising price by 17%; programs airing 

during the last hour of prime-time command 14% more advertising dollars per spot; and 

programs with teens or children get discounted at 11%. 

VI. Estimating the Advertising Premia

We can now compare the marginal effect that each characteristic has on advertising 

price at the mean. We set all other characteristic values to their unconditional means9, 

and then compare the effect of changing the characteristic dummy from zero to one. 

Table 5 lists the premia (discounts) of seven different content dummy variables. We find 

that broadcast television programs receive large and statistically significant content 

premia (discounts), holding constant the number, income, age, and gender of the 

viewers these programs attract. Sitcoms receive large premia, while news shows and 

police dramas receive large discounts. In fact, sitcoms earn over $75,000 more per 30-

second commercial spot than Police Dramas and News Magazines, even after adjusting 

for the size and demographics of the audience. This certainly comports with our 

intuition of advertisers’ preferences and in accordance with Sunstein’s conjecture that 

“advertisers want programming that will put viewers in a receptive mood, and hence 

not be too ‘depressing’”. 

 

9 Unless these unconditional means conflict with the characteristic value. For instance, the Police Drama 

and News Magazine dummies must be zero when the Sitcom dummy is one, so we compare the effect of 

changing the Sitcom dummy from zero to one with the Police Drama dummy and News Magazine dummy 

set to zero in both cases.
15

Table 5

Premia for Different Characteristics

Content Dummy 

Variables

Premium 

(Discount)

Sitcom 37,533.72

News (38,634.64)

Police (38,677.33)

Genx 30,633.44

Ethnic 15,384.11

Teens or Children (20,937.09)

Sports 16,242.81 

Advertisers prefer programs that have “light” content to programs with heavier or 

more difficult content, even adjusting for the number and types of viewers these 

programs attract. This leads to a bias towards more sitcoms and generally “light” fare 

and against news programming and other “dark” fare. In their model, Anderson and 

Coate find that advertiser preferences can lead to non-optimal programming patterns if 

certain programming raises consumers’ willingness to pay for products. In our sample, 

there were 38 sitcoms, 7 news magazines, and 5 police dramas out of a total of 71 

programs. Even adjusting for the fact that sitcoms are only half as long as most news 

shows and police dramas, sitcoms aired over one-and-a-half-times as often as news 

programming and police dramas combined. Anderson and Coate demonstrate that 

advertiser preferences can distort programming in a model with one channel and two 

available types of programming. 

Does their insight hold in a universe of multiple channels and multiple program 

types? Certainly the proliferation of multiple all-news cable channels demonstrates that 

a significant portion of the viewing population does not receive their preferred amount 
16

of news programming from broadcast sources. Do advertiser biases lead to 

programming patterns that many viewers find less desirable? Do we find a very large 

block of viewers under-served by existing network programming? A close examination 

of the cable network HBO (Home Box Office) helps provide some answers to these 

questions. 

VII. HBO’s Programming Strategy

Advertisers apparently prefer certain programming content because such content 

raises the viewers’ probability of purchase. We have shown that, even adjusting for the 

number and demographics of viewers, advertisers prefer sitcoms to news programming 

and police dramas, which leads networks to air more sitcoms. We now wish to examine 

whether significant blocks of viewers therefore go un-served by network programming 

offerings. Certainly, finding that a pay television provider attracts extremely large 

blocks of paying viewers by deliberately counter-programming against the broadcast 

networks would support our theory. 

Journalist Tad Friend discusses HBO’s programming strategy in The Next Big Bet, an 

article from the May 14, 2001 issue of The New Yorker. The article discusses HBO’s 

programming strategy as it relates to the writers of Six Feet Under, whose story line 

revolves around a family that owns and operates a funeral home. 

Many advertisers apparently avoid programs containing conflict and subtlety 

because these programs do not raise viewers’ probability of purchasing advertised 

products as effectively as more upbeat programming. As Alan Ball, the head writer of 

Six Feet Under, who wrote for the network sitcom Cybill before penning the movie 

American Beauty observes:

“ . . . the reason why <sic> network executives always tell you to make 

the story softer, to iron out the edge, is that network TV exists as a 

vehicle for marketing, so they want . . . an audience that’s cheerful, 

chuckling, primed by the fantasy in the shows for the fantasy of the 

products. . . . I’ve tried to be that kind of writer, but I resented having to 

modify my stories so they’d be a more efficient delivery vehicle for 

Burger King.”10

Ball goes on to describe broadcast networks’ programming philosophy: “After a while, 

you realize that all network notes can be reduced to: one, make everybody nicer, remove 

all the conflict; and, two, articulate the subtext, have somebody state what’s going on.

If a large group of viewers prefer darker, subtler material, and advertiser-supported 

programming does not deliver such material, then pay programming networks can 

successfully follow a counter-programming strategy, by airing darker, more difficult

content in order to capture this large niche of viewers. HBO seeks to capture viewers 

who do not care for network options, following a counter-programming strategy against 

 

10 Tad Friend, “The Next Big Bet,” The New Yorker, May 14, 2001, p. 84.
17

network programming. Scott Sassa, president of NBC West Coast, comments on HBO’s 

business model: “A pay-cable network like HBO is trying to fill a need for people who 

feel under-served by network television.”

HBO, according to the article, deliberately seeks to “ . . . repudiate the formulas . . . 

on . . . sitcoms;” making its original programs like The Sopranos and Six Feet Under darker 

and more complex in order “ . . . to be the strongest possible counterpoint to network 

television.” HBO even mocks advertising itself: “The pilot (of Six Feet Under) is 

punctuated by fake commercials for mortuary products such as ‘wound filler’ and a 

deluxe hearse. . . . ”

As these quotations demonstrate, HBO deliberately counter-programs against 

network biases toward “lighter” programming preferred by advertisers, going to great 

lengths to differentiate its own programming from the broadcast networks. Spence and 

Owen (1977) find that even pay television under-serves small groups of viewers with 

intense preferences. Therefore, HBO must serve a large number of viewers. HBO now 

reaches 26 million homes, receiving $5 per month of the $10-$13 per month paid by 

subscribers. HBO is a “premium” cable channel, meaning that viewers choose whether 

or not to buy HBO on an a-la-carte basis, and do not purchase HBO as part of a package. 

The large number of viewers paying directly for HBO’s programming suggests that 

network programming under-serves a very large portion of the viewing public. 

VIII. Strategic Approaches

The strategic management literature often divides media business strategies into two 

categories: richness and reach. Richness refers to quality of media content as defined by 

the consumer or end user. This quality can refer to accuracy, sophistication, depth, 

relevance and customization. A focus on richness produces strong product 

differentiation, which gives the firm market power over a smaller group of viewers that 

values such content. Reach in the context of media strategy refers to maximizing the 

number of viewers who watch the program. Thus, emphasizing reach produces a media 

product that appeals to a larger number of viewers (Evans and Wurster, 1997), while an 

emphasis on richness creates a media product that is valued intensely by a smaller 

number of viewers. 

In their work, Evans and Wurster (1997) contend that there is a fundamental conflict 

between richness and reach. A media firm can either produce content that appeals to 

many viewers, selling this large viewer group to advertisers, or they can choose to 

produce content that appeals strongly to fewer viewers, and charge these viewers 

directly for the content. Therefore, Evans and Wurster argue that there is a trade off 

between richness and reach. As shown in this paper, advertiser preferences introduce 

new subtleties into the relationship between richness and reach. The limitations that 

advertisers place on richness also limit the broadcasters’ reach, leaving significantly 

large groups of viewers that prefer richer content going un-served by the broadcasters’ 

program offerings, allowing pay providers like HBO to attract 26 million viewers by 

airing richer content. 
18

IX. Conclusion

Broadcasters supply a public good for profit. Broadcasters deliver a non-excludable 

good (programming) to viewers, combine the good with advertising, and sell 

commercial time to advertisers who desire access to viewers. As theory predicts, market 

failures may occur when markets supply a public good. One such market failure is the 

distortion in program choice stemming from advertisers’ preferences. Advertisers 

prefer programming content that best “frames” their advertising. Such content tends to 

be light and “unchallenging”. Viewers preferring darker and more challenging content 

go under-served. Using a unique data set, we found that advertisers pay a premium for 

sitcoms and programs with younger casts, while news programming and police dramas 

receive a discount. Of the programs in our sample, there were seven news magazines, 

five police dramas, and 38 sitcoms, so that advertisers’ preferences appear to drive 

programming content. HBO’s anti-network counter-programming strategy provides 

further evidence that advertiser-supported networks under-serve a large group of 

viewers. Twenty-six million people pay at least $10 per month for HBO. 

Indeed, the growth of cable itself points to an interesting phenomenon. Some 

economists have argued that market failures create profit opportunities for innovations 

which privatize a public good. As an example supporting this view, Anderson and Hill 

(1975) cite the invention of barbed wire, an innovation that enabled the inexpensive 

delineation of property rights in the Western United States. To the extent that 

broadcasters cannot deliver the optimal type and amount of programming to viewers, 

other firms may develop innovations allowing viewers to purchase more preferred 

programming directly from providers. Indeed, the development and rapid spread of 

cable and satellite television can be attributed at least in part to the market failures 

inherent in over-the-air, advertiser-supported television. 
19

References

Anderson, Simon and Coate, Stephen (2000), “Market Provision of Public Goods: The 

Case of Broadcasting,” NBER Working Paper 7513.

Anderson, Terry, and Hill, Peter (1975), “The Evolution of Property Rights: A Study of 

the American West,” Journal of Law and Economics, 18 (1), 163-178.

Becker, Gary and Murphy, Kevin (1993), “A Simple Theory of Advertising as a Good or 

a Bad,” Quarterly Journal of Economics, 108 (4), 941-964.

Brown, Keith and Cavazos, Robert (2002), “Network Revenues and Minority Television 

Programming,” Journal of Media Economics, 15(4), 227-239.

Evans, Phillip B., and Wurster, Thomas (1997), “Strategy and the New Economics of 

Information,” by Phillip B. Evans and Thomas S. Wurster. Harvard Business Review, 

September 1, 1997. 

Fisher, Franklin, McGowan, John, and Evans, David (1980), “The Audience-Revenue 

Relationship for Local Television Stations,” The Bell Journal of Economics, 11 (2), 694-

708.

Friend, Tad (2001) “The Next Big Bet,” The New Yorker, May 14, pp. 80-87.

Gabszewicz, J, Laussel, Didier, and Sonnac, Nathalie (1999), “TV-Broadcasting 

Competition and Advertising,” mimeo, CORE.

Goettler, Ronald (1999), “Advertising Rates, Audience Composition, and Competition in 

the Network Television Industry,” GSIA Working Paper #1999-E28.

Goettler, Ronald and Shachar, Ron (2000), “Spatial Competition in the Network 

Television Industry,” mimeo, Carnegie Mellon University.

Seldon, Barry and Jung, Chulho (1993), “Derived Demand for Advertising Messages 

and Substitutability among the Media,” Quarterly Review of Economics and Finance, 33 

(1), 71-86.

Shachar, Ron and Emerson, Jay (2000), “Cast Demographics, Unobserved Segments, and 

Heterogenious Switching Costs in a Television Viewing Choice Model," Journal of 

Marketing Research, 37 (2), 173-186.

Silk, Alvin, Klein, Lisa, and Berndt, Ernst (2002), “Intermedia Substitutability and 

Market Demand by National Advertisers,” Review of Industrial Organization, 20(4), 

323-348

Spence, Michael and Owen, Bruce (1977), “Television Programming, Monopolistic 

Competition, and Welfare,” Quarterly Journal of Economics, 91 (1), 103-126.

Steiner, Peter (1952), “Program Patterns and Preferences, and the Workability of 

Competition in Radio Broadcasting,” Quarterly Journal of Economics, 66 (2), 194-223.

Sunstein, Cass (1999), “Private Broadcasters and the Public Interest: Notes Toward a 

‘Third Way’,” mimeo, University of Chicago.