***[Federal communications commission]{.underline}***

***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

**Empirical Aspects of Advertiser Preferences and Program**

**Content of Network Television**

KEITH S. BROWN[^1]

Federal Communications Commission

Roberto J. Cavazos[^2]^\*^

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.

**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

"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 firms[^3]. 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 good[^4]. 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
media[^5]. 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 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
over-duplicate 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 under-serve 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, 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 counter-program (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
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-elasticity[^6] 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 cross-elasticity 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
advertiser-supported 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 $\omega > 0$. The desirability of the
product is given by $\sigma \in \lbrack 0,\overline{\sigma}\rbrack$
where $\overline{\sigma} < 1$, which represents the proportion of
consumers who desire the product at price $\omega$. Therefore,
advertisers are willing to pay their expected profit gained from an
advertisement, $\text{σω}$, 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

> ![](media/image1.wmf)(1)

To simplify our estimation, we assume that ![](media/image2.wmf) (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$p = \omega\overline{\sigma}$. 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$\omega$. If we represent
these content and demographic characteristics as a vector
![](media/image3.wmf), then the per-viewer price of advertising is

![](media/image4.wmf) (2)

and we estimate each value for ![](media/image5.wmf).

**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 31^st^, 1995
issue of *Broadcasting and Cable* provided forecasts of program shares
for the 1995-96 season, while the August 14^th^, 1995 issue of
*Advertising Ag*e 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
Prime-Time 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 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 ![](media/image6.wmf).
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
$P = n^{b}\omega\overline{\sigma} \cdot (\delta_{1} + \text{.}\text{.}\text{.} + \delta_{n})$,
where $b$ reflects the likelihood of viewer duplication across
programs[^7]. 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:

$$\begin{array}{r}
\text{Ln}(\text{Adprice}) = B_{1} + B_{2}(\text{ln}\text{share}) + B_{3}(\text{ln}\text{total}) + B_{4}(\text{PerUp}) + B_{5}(\text{PerYoung}) + B_{6}(\text{GenConc}) \\
 + B_{7}(\text{Sitcom}) + B_{8}(\text{news}) + B_{9}(\text{Police}) + B_{\text{10}}(\text{genx}) + B_{\text{11}}(\text{ethnic}) + B_{\text{12}}(\text{torc}) + B_{\text{13}}(\text{sports}) + B_{\text{14}}(\text{late})
\end{array}$$

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.

**Table 1**

**Definition of Explanatory Variables**

  ---------------- -------------------------------------------------------
  *Percentage      Continuous variable which is the percentage of a
  Upper Income*    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      Continuous variable which measures the percentage of a
  18-34*           program's viewers that are aged 18-34. (Advertisers may
                   prefer younger viewers).

  *Gender          A variable that equals (percentage female-percentage
  Concentration*   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        Do advertisers prefer programs with teens or children
  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.
  ---------------- -------------------------------------------------------

**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 $\lambda = - \text{.}\text{03}$ 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
$\lambda = 0$ (which would be our specification), we get a Chi-square
value of 0.03 and only a 13% probability that $\lambda = 0$ 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 errors[^8].

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
.008, is positive and significant, indicating that advertisers strongly
prefer concentrated gender groups[^9].

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*

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,

![](media/image4.wmf)

Each $\delta$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 $\delta$for the dummy characteristics
with statistically significant coefficients[^10]. 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** $\delta's$

**(For Statistically Significant Program Characteristics Only)**

*\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_*

$$\begin{array}{r}
\delta_{\text{Sitcom}} = e^{B_{7}} = 1\text{.}\text{219} \\
\delta_{\text{news}} = e^{B_{8}} = 0\text{.}\text{775} \\
\delta_{\text{Police}} = e^{B_{9}} = 0\text{.}\text{774} \\
\delta_{\text{genx}} = e^{B_{\text{10}}} = 1\text{.}\text{172} \\
\delta_{\text{torc}} = e^{B_{\text{12}}} = 0\text{.}\text{889} \\
\delta_{\text{late}} = e^{B_{\text{14}}} = 1\text{.}\text{137}
\end{array}$$

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

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 means[^11], 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'".

**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
  ---------------------------------- ------------------------------------

![](media/image7.wmf){width="6.944444444444445e-4in"
height="6.944444444444445e-4in"} 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 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."[^12]

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 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.

**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.

**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
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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.

[^1]: ^\*^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.

[^2]: ^\*\*^ 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]: Here, we define a public good as a good that is non-excludable and
    non-rival in consumption.

[^4]: 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.

[^5]: 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.

[^6]: 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.

[^7]: ![](media/image8.wmf), where *z* represents the total number of
    different programs, each reaching $x$viewers, that are required to
    reach $y > x$viewers.

[^8]: 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.

[^9]: 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.

[^10]: 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
    $B_{i}e^{B_{i}x_{i}}$.

[^11]: 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.

[^12]: Tad Friend, "The Next Big Bet," The New Yorker, May 14, 2001, p.
    84.