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Media Ownership Study 8a-Submitted Study

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Released: July 27, 2011


Local Media Ownership and Viewpoint Diversity in Local Television News





April 2011




Adam D. Rennhoff
Assistant Professor, Jones College of Business
Middle Tennessee State University
P.O. Box 27
Murfreesboro, TN 37132
615-898-2931
rennhoff@mtsu.edu



Kenneth C. Wilbur
Assistant Professor, Fuqua School of Business
Duke University
100 Fuqua Drive, Box 90120
Durham, North Carolina 27708-0210
919-926-8536
Kenneth.Wilbur@Duke.edu
http://kennethcwilbur.com/">Http://Kennethcwilbur.com



We thank the Federal Communications Commission for providing data, and Jonathan Levy and
Tracy Waldon for many helpful discussions. Wagner Kamakura and Carl Mela provided
valuable suggestions. Any remaining errors are our own.



Executive Summary

This study proposes a novel, market-based measure of viewpoint diversity in local television
news. It then investigates a panel dataset of media markets to determine whether changes in
viewpoint diversity are associated with changes in local media market ownership structure. The
results are estimated with reasonable precision but statistically indistinguishable from zero.

Introduction

This study was commissioned by the United States Federal Communications Commission
(“FCC”) as part of its 2010 Quadrennial Review of Media Ownership Rules. Its purpose is two-
fold.
First, it proposes a new market-based measure of viewpoint diversity in local television
news programs. The market-based approach is desirable because it relies on consumers’ actions
to define viewpoint diversity. However, it is complicated by the fact that consumers’ actions
depend on consumer preferences as well as media content. The key to the approach is to use
local viewing of national news programs to learn about local market preferences. This allows
local media content to be distinguished from local market preferences.
Second, the analysis uses a descriptive regression to relate changes in the proposed
viewpoint diversity index to changes in local media cross-ownership, co-ownership and
ownership diversity. The associations between viewpoint diversity and ownership variables are
all statistically indistinguishable from zero and likely to be smaller than 10% in absolute value.
Section 1.1 gives a brief overview of the Media Ownership Rules that the FCC is
currently reviewing. Section 1.2 discusses the history of defining viewpoint diversity, so that the
reader may understand the proposed definition in context. Section 2 defines the proposed
measure of viewpoint diversity. Section 3 presents the empirical approach of relating changes in
viewpoint diversity within a market to changes in local media market ownership structure.
Section 4 contains the estimation results and section 5 concludes by relating the empirical results
to the rules the FCC is reviewing.

1.1. Media Ownership Rules
Three media ownership rules are relevant to the present analysis. This section gives just a brief
overview of the rules. FCC (2010) andhttp://edocket.access.gpo.gov/cfr_2003/octqtr/pdf/47cfr73.3555.pdf"> 47 CFR 73.3555 are more expansive.
1


Newspaper/Broadcast Cross-Ownership Rule

: Since 1975, the FCC has restricted the
common ownership of a broadcast station and a newspaper when, roughly speaking, the
station’s footprint contains the newspaper’s distribution area. Waivers to this rule may be
granted when common ownership is judged to be aligned with the public interest. In
2007, the waiver criteria were relaxed so that common ownership would be presumed to
be not inconsistent with the public interest in the 20 largest media markets, so long as the
TV station is not among the four largest in the market and there would be at least eight
post-merger “voices” available in the market.1 Common ownership is still presumed to be
inconsistent with the public interest in smaller media markets unless (1) one of the two
media outlets were “failed” or “failing,” or (2) the joint entity would significantly
increase the amount of news available in the market.

Local TV Ownership Limit

: One entity may own two television stations within the same
market if (1) their signals do not overlap (this case is rare), or (2) one of the stations is not
ranked in the top four stations in the market based on market share, and there are at least
eight independently-owned stations in the market. This second provision essentially rules
out dual station ownership in smaller markets, as they are typically served by fewer than
eight stations.

Local Radio/TV Cross-Ownership Rule

: In markets with at least 20 independently-owned
voices, one entity may own one TV station and up to seven radio stations or two TV
stations and up to six radio stations, subject to the Local TV Ownership Limit. In markets
with 10-19 independently-owned voices, one entity may own up to two TV stations and
up to four radio stations. In markets with 9 or fewer independently-owned voices, an
entity that owns a TV station may not own more than one radio station.

1.2. Viewpoint Diversity
The FCC’s policy objectives are competition, localism and diversity. The FCC’s diversity
policymaking objective is nuanced and sometimes controversial. It is motivated by the
observation that, since the public owns the airwaves on which television and radio signals are
broadcast, the media should serve all segments of the population. The need for regulation is
implied by the well known result that some types of content may be underprovided by a

1 A “voice” may be a TV station, radio station, newspaper or a cable system.
2


competitive market. As the US Supreme Court noted in AP v. United States, “[the First]
Amendment rests on the assumption that the widest possible dissemination of information from
diverse and antagonistic sources is essential to the welfare of the public, that a free press is a
condition of a free society.” The Court justified media ownership regulations to preserve this
freedom, saying “freedom to publish is guaranteed by the Constitution, but freedom to combine
to keep others from publishing is not.” http://supreme.justia.com/us/326/1/case.html">(326 U. S. 1)
The FCC has operationalized its diversity objective in five ways (FCC 2010):

Outlet diversity

is the number of independently-owned media outlets.

Source diversity

is the availability of media content from a variety of content creators.

Minority and female ownership diversity

is the number of media outlets owned by minority
race/ethnic groups and women.

Program diversity

is the variety of program formats and content provided by the media.

Viewpoint diversity

is the availability of content reflecting a variety of perspectives.

The first three definitions reflect the concept of source diversity, that is, increasing the
number of voices available in the media market. Source diversity is fairly straightforward to
define and measure. The final two definitions reflect the concept of content diversity, that is,
increasing the number of types of programs and opinions that are available in the media market.
Source diversity has sometimes been seen as a standalone policy objective, and it has sometimes
been seen as a means to achieve content diversity.

The purpose of the present analysis is to determine whether media co-ownership, cross-
ownership and ownership diversity within a market are associated with viewpoint diversity in
that market’s television news. It is emphasized that the analysis seeks to examine viewpoint
diversity, not program diversity.

Empirical analysis of viewpoint diversity requires a measure of viewpoint diversity. The
“availability of content” component of the definition is fairly straightforward but a “variety of
perspectives” component is not. What qualifies as a “perspective?” And what constitutes a
“variety” of perspectives?

This analysis is not the first to grapple with the question of how to define viewpoint
diversity. The Newspaper/Broadcast Cross-Ownership Rule was challenged shortly after its
passage in 1975. The Court upheld the rule, noting that “the regulations, which are designed to
promote diversity of mass media as a whole, are based on public interest goals that the FCC is
3


authorized to pursue.” The court went further to note that “diversity and its effects…are elusive
concepts, not easily defined let alone measured without making quality judgments that are
objectionable on both policy and First Amendment grounds.” http://supreme.justia.com/us/436/775/case.html">(436 U. S. 775; emphasis added)
As McCann (2010) put it, “In other words, the court didn’t require the FCC to specifically define
viewpoint diversity, instead relied on the FCC’s rational judgment based on experiences.”

In 2003, the FCC relaxed its ownership rules substantially. It eliminated cross-media
ownership regulations in media markets with eight or more television stations, and allowed
newspaper/television/radio cross-ownership in media markets served by four to eight television
stations. This action was justified by an analysis based on the “Diversity Index,” which sought to
measure viewpoint diversity in a manner inspired by the Herfindahl-Hirschmann Index (HHI)
that antitrust authorities use to gauge market competitiveness. The Diversity Index used
consumers’ average time spent with each medium to weight its importance. It then assigned
equal “market shares” to each outlet within each medium and combined those “market shares”
for commonly owned outlets. For example, New York was served by 23 television stations, so
each television station was assigned a “market share” of 4.3% (or 1/23). Finally, based on these
weights and “market shares,” the Diversity Index was calculated using a sum-of-squares
approach similar to the HHI.

The ownership rule relaxation was challenged in court immediately and quickly
overturned. In Prometheus Radio Project vs. FCC, the 3rd Circuit Court was emphatic on its view
of the Diversity Index. It ruled that “the Commission did not justify its choice and weight of
specific media outlets.” Further, “the Commission did not justify its assumption of equal market
shares.” And, “the Commission did not rationally derive its Cross-Media Limits from the
Diversity Index results.” http://law.justia.com/cases/federal/appellate-courts/F3/373/372/474282/">(373 F.3rd 372)

The proposed definition of viewpoint diversity in this paper should be understood in light
of these past difficulties. When this concept was used to justify media ownership restrictions in
the 1970’s, it was not precisely defined. The FCC’s one attempt to measure this concept in 2003
was rejected rather quickly.

Despite these difficulties, it is important to try to measure important policymaking
criteria. Unmeasurable policy objectives lead to inevaluable policies. The next section
undertakes this challenge.

4


2. A Market-Based Measure of Viewpoint Diversity
This section proposes a market-based measure of viewpoint diversity. Section 2.1 explains the
use of a market-based measure. Section 2.2 explores some intuitive properties that any
reasonable measure should exhibit, and it shows the difficulty of separating the viewpoint
diversity expressed in the media from the preferences exhibited by the audience of the media.
Section 2.3 defines the proposed measure formally, and section 2.4 shows how viewership for
national news programs can be used to separate local preferences from local news program
characteristics. Section 2.5 discusses the limitations of the proposed definition.

2.1. Basis for Measurement
In considering the question of how to measure the variety of perspectives offered among a set of
media programs, one might quite naturally start by thinking about conducting a content analysis.
For example, one could use computers or human coders to analyze samples of media content and
encode the perspectives expressed in each sample.

While intuitive, such content-based approaches to diversity measurements face three
difficulties. First, accurate content quantification is quite difficult. Human collection of content
data is labor-intensive and may be subjective, and is therefore costly and slow. Computer
collection of content data can be performed quickly but may fail to capture aspects of the content
which are important but difficult to quantify. Second, time and cost constraints force the
researcher to decide which aspects of content to encode, and those decisions may be at odds with
the aspects of content that actually matter to consumers. Third, measures which are based solely
on media content cannot predict how different audiences would react to the same content.

Consider a thought experiment to illustrate this final point. Suppose there are two
subjective issues, 1 and 2, and two markets, A and B. Suppose everyone in market A is interested
in issue 1 and everyone in market B is interested in both issue 1 and issue 2. Suppose a news
program in market A uses four minutes of program time to present four perspectives on issue 1.
Suppose a news program in market B uses two minutes to present two perspectives on issue 1
and another two minutes to present two perspectives on issue 2. A content-based measure of
viewpoint diversity might well conclude that the news program in market A exhibits greater
diversity, since more perspectives about issue 1 were expressed. However, from a policy
perspective, it might be argued that the two news programs served their markets equally well
5


given market preferences and time constraints. Yet this conclusion depends on information about
market preferences, and therefore would be very difficult to draw with a purely content-based
measurement of viewpoint diversity.

More generally, this is why viewpoint diversity is so difficult to define and measure. It is
subjective, depending as much on the preferences of the audience as on the contents of the
media.

The market-based approach alleviates all three problems of content-based diversity
measurements. It obviates a burdensome data collection task, eliminates the need to predetermine
what content characteristics are important, and it relies on consumers’ observed choices which
embed market-specific preferences.

By presenting these limitations of content analysis, it is not the authors’ intention to
diminish the validity of content-based measures of viewpoint diversity. To the contrary, content
analysis is a worthwhile and informative exercise. For example, Gentzkow and Shapiro (2010)
invented a brilliant means of avoiding the primary limitations of content analysis, finding that
media outlets’ use of political language typically reflected their customer bases’ preferences.
Further, market-based approaches, including the one proposed here, have their own limitations,
as discussed in section 2.5.
The view of the authors is that policymakers and judges should consider both content-
and market-based approaches to measuring viewpoint diversity. Each type of approach should be
evaluated with a rational understanding of its strengths and weaknesses and the degree to which
those strengths and weaknesses affect the specific application of the method.

2.2. Intuitive Properties
This section considers a series of thought experiments to motivate and justify the viewpoint
diversity measure.

Suppose two competing television stations, A and B, within a market each offer a local
news program, and suppose that each station has a 50% share of the local news audience. The
following two extreme possibilities are fundamentally different but observationally equivalent.
1) The market’s television audience consists of two equally sized segments with polar opposite
viewing preferences. The observed 50/50 audience split suggests that each local news program is
6


tailored to one segment’s preferred viewpoint. This would be consistent with high viewpoint
diversity among the programs provided by the media market.
2) All viewers in the market have the same preferred viewpoint. The two stations offer this
preferred viewpoint. Since they offer essentially identical programs, they split the market again,
with half the viewers watching station A and half watching station B. This would be consistent
with low viewpoint diversity among the programs provided by the media market.

The observational equivalence of these extreme possibilities illustrates the primary
difficulty in measuring viewpoint diversity. Audience data on local news viewing alone cannot
provide a measure of viewpoint diversity, since media consumption choices are based on both
viewer preferences and media content. Since the concept of viewpoint diversity is fundamentally
subjective, its measurement must account for the preferences of the group receiving the
viewpoints.

Another thought experiment can show how this difficulty will be resolved. Suppose that
each of the two television stations offers a national news program in addition to its local news
program. Assume that the national news programs offer different viewpoints, that they air
unmodified in each of many local media markets and that each garners a 50% rating nationwide.
Now, consider two subcases of this model.

First, suppose that the national news program on station A garners a 80% share of
viewers in a particular local market, and the national news program on station B garners a 20%
share of viewers in that market. Further, suppose that the two local news programs split the local
audience with a 50% audience share each. It is clear that, relative to the national market, the local
market has a strong, homogeneous preference for viewpoints of the type provided in station A’s
national newscast. Since the two local newscasts split the local market, their content must be
roughly similar, indicating a low level of viewpoint diversity.

In the second subcase, suppose the national news programs on stations A and B split the
local audience, each with a 50% share of local viewers. This indicates that the variety in local
consumers’ preferred viewpoints roughly matches the variety in national consumers’ preferred
viewpoints. Further, suppose the local news program on station A is watched by 80% of the
market while the local news on station B is watched by 20% of the market. This information will
tell us that stations A and B are providing programs that contain very different viewpoints. This
is because viewers in the local market exhibit some heterogeneity in viewpoint preferences but
7


the local news on station B is so far away from the preferred viewpoint of the average consumer
that few consumers watch it.

These examples convey the intuition underlying the market-based measure of viewpoint
diversity. It will weigh dispersion in local market shares for local news programs against
dispersion in local market shares for national news programs. The latter indicates the degree to
which the local market’s preferences differ from the national market, and this will distinguish
between the two observationally equivalent extreme cases discussed above.

2.3. A Market-Based Viewpoint Diversity Index
This section defines a viewpoint diversity index for local news programs. It shows how to
recover this index from program audience data, and shows that it cannot be empirically separated
from the dispersion in local tastes, as illustrated in the examples above.

Consider a market m that is served by three local news programs, indexed in order of
ascending market share by j
,
1
3
,
2 . Assume that the programs are differentiated by a single
dimension of viewpoint diversity, as in Hotelling (1929). The range of possible viewpoints can
then be represented by a single horizontal line, and the viewpoint expressed by each program j in
market m may be represented by a point m
x on that line.2 Assume for simplicity that the
j
programs are ordered such that program 1 is closest to the left side of the line and program 3 is
closest to the right side of the line.

Let x represent a point on the horizontal line denoting the preferred viewpoint of viewer
i
i. These points are assumed to be distributed Normal with mean
and variance 2 .3 The
m
m
Normal distributional is less tractable than the typical assumption of uniform preferences but is
more realistic. Consumer i gets utility m
u from watching program j ,
ij
m
u
V |
m
x
x | ,
(1)
ij
i
j

2 Higher-order viewpoint spaces are not considered because the available data do not allow for the nonparametric
identification of additional dimensions of program differentiation. The single line in the model could be thought of
as the first principal component of a higher-dimensional viewpoint diversity space.
3 Below, an assumption is made that the national distribution of preferred viewpoints is Standard Normal. Neither
assumption is necessary-and-sufficient for the other to hold, but they are compatible if viewers’ preferred viewpoints
are imperfectly correlated with their locations and if the moments of the national distribution are compatible with the
moments of the market-specific distributions, for example, if the weighted sum of market-specific mean viewpoint
preferences is zero, where the weights represent the percentage of the national population contained within each
market.
8


where V is the value of watching the news and m
x is the location of the viewpoint expressed in
j
the local news program j . It is assumed that each viewer watches the program whose viewpoint
is closest to her preferred viewpoint. It is also assumed that V is large enough that the market is
fully covered, i.e., that all consumers who want to watch local news watch one of the available
local news programs. This assumption is the primary limitation of the proposed approach and is
discussed in depth in section 2.5.

A useful theoretical construct is the point at which a consumer is just indifferent between
watching programs 1 and 2, m
xˆ . Setting m
m
u
u shows that ˆ m
x
( m
m
x
x ) / 2 . Similarly, the
12
i1
i 2
12
1
2
point of indifference between programs 2 and 3 is ˆ m
x
( m
m
x
x ) / 2 .
23
2
3

The proposed Viewpoint Diversity Index D is defined as the difference between these
m
two points of indifference:


m
m
D
xˆ
xˆ
(2)
m
23
12
Figure 1 provides the intuition underlying this measurement of viewpoint diversity. It shows that
the news programs in market m provide less diversity than those in market m' , since they cover
less of the line. Accordingly, the diversity index D is greater than D .
m'
m

A few remarks may help explain the Viewpoint Diversity Index. First, the definition in
equation (2) is proportional to the entire span of viewpoints available in the market, m
m
x
x . It
3
1
is written in terms of the points of indifference because the audience shares must sum to one, so
the three audience shares in the data really provide only two degrees of freedom. Writing the
diversity index in terms of the two points of indifference makes this fact more salient and shows
that other dispersion indices, such as a standard deviation based on the three news program
locations, are not advisable. Second, notice that it is based purely on station locations. Local
market preferences, as represented by distributional parameters
and
, do not enter the
m
m
index. Third, notice that if either station 1 or station 3 changes its location in viewpoint space,
the diversity index will change its value. However, it will not change if station 2 moves, since
this would result in a reallocation of market shares without altering the range of viewpoints
provided by the marketplace. Fourth, the index is independent of the scale of available
viewpoints. That is, the index is the same for ( m
x , m
x , m
x )
,
1
(
)
3
,
2
as it is for
1
2
3
9


( m
x , m
x , m
x )
)
13
,
12
,
11
(
. Finally, while the Viewpoint Diversity Index will always be positive,
1
2
3
there are no benchmark values that take on special meaning.

To calculate the viewpoint diversity index, it is necessary to determine the program
locations in the viewpoint space. This may be done by relating the predicted audience shares in
the model to data. The market share of program 1 is given by the probability mass of viewers
whose preferred viewpoints lie to the left of m
xˆ ,
12
m
s
(( ˆm
x
) /
) .
(3)
1
12
m
m
where
is the standard normal cumulative distribution function. Similarly, the market share of
program 3 is given by the probability mass of preferred viewpoints to the right of m
xˆ ,
23
m
s
1
(( ˆm
x
) /
)
(4)
3
23
m
m
Presuming m
s and m
s are available in the data, the points of indifference can be recovered from
1
3
equations (3) and (4) as
m
1
m
xˆ
(s )

(5)
12
m
1
m
m
1
m
xˆ
1
(
s )

(6)
23
m
3
m
These can be substituted into (2) to show that the empirical Viewpoint Diversity Index is
D
(
1 1
(
m
s )
1 ( m
s )) .
(7)
m
m
3
1
Equation (7) shows, formally, the indeterminacy between program dispersions and market-
specific tastes. With data from a single market, it will be impossible to separate the program
locations from the dispersion in market-specific tastes,
. The next section shows how this
m
problem may be resolved using local audience shares for national news programs.

2.4. Recovering Local Preferences
This section uses local viewership of national news programs to separate local preferences from
local stations’ viewpoint diversity.

It is assumed that all three national news programs are available in many local markets
and are indexed with k
( ,
A B, C) in ascending order of national audience share. It is assumed
that these news programs are differentiated on the same viewpoint scale as the local news
programs. This assumption is not innocuous. If the viewpoint diversity expressed in national
news programs is of a fundamentally different nature than that expressed in local news programs,
10


then the approach proposed here will not work. The arguments in favor of this assumption are as
follows. First, the national news almost always immediately follows or precedes the local news.
Since the two programs’ audiences overlap, attributes that the audience finds important in one
program may also be the attributes that the audience finds important in the other program.
Second, because these are two news programs, they are likely to share many characteristics in
common, such as the types of stories they cover and the possible styles or slants available in their
coverage of those stories. Third, both local and national news use some of the same publicly
available video footage for some of the stories they cover, so some of the main inputs to the two
types of programs are the same.

For simplicity, assume national news program A is closest to the left side of the line and
program C is closest to the right side of the line. Note that national news program A does not
necessarily correspond to local news program 1, and that the two positions of the national and
local news programs on a particular station need not be correlated.

To anchor the location and scale of preferences, it is assumed that the national
distribution of consumer viewpoint preferences is Standard Normal. Under these assumptions,
the locations of the indifferent viewers for national news programs in viewpoint space are given
by equations (5) and (6) as
ˆ N
1
x
( N
s )
(8)
AB
A
ˆ N
1
x
1
(
N
s )
(9)
BC
C
where N
s is the fraction of all national news viewers (in all markets) tuned to the national news
k
program on network k.

Let m
s be the fraction of local news viewers in market m who watch the national news
k
program on local channel k . Since (8) and (9) pin down the points of indifference among
national news programs, equations (10) and (11) relate those locations to the local market shares
of the national news programs:
N
m
xˆ
1 (s )

(10)
AB
m
A
m
N
m
xˆ
1 1
(
s )
.
(11)
BC
m
C
m
Equations (10) and (11) now can be solved for local preference parameters:
ˆN
x
ˆN
x

BC
AB

(12)
m
1 1
(
m
s )
1 ( m
s )
C
A
11



ˆN
1
x
( m
s ) .
(13)
m
AB
m
A
Equations (8), (9), and (12) can be substituted into (7) so that the Viewpoint Diversity Index
may be expressed purely in terms of data on local audience shares of local news programs, local
audience shares of national news programs, and national audience shares of national news
programs.

2.5. Limitations
The primary limitation of the proposed Viewpoint Diversity Index is that it excludes the idea of
“vertical differentiation” in news programming. Vertical differentiation refers to news program
attributes that all consumers like. For example, it may be that spending more money on special
effects, presenters, set design or wardrobe would lead to higher viewing among all consumers,
regardless of their viewpoint preferences. This extension was considered but rejected as
infeasible. An outline of the reasons is given.

First, consider how the diversity statistic in equation (2) is calculated. Two degrees of
freedom in national viewership of national news programs are used to pin down the two points of
indifference between the three national news programs. These two points of indifference are
used, in conjunction with the two degrees of freedom available in local viewership of national
news programs, to pin down two moments of the distribution of local viewpoint preferences.
Finally, all of these inferences are used along with the two degrees of freedom available in local
viewership data of local news programs, to pin down the two points of indifference between the
three local news programs provided in each media market.

In the previous paragraph, it was assumed at every step that each news viewership market
was fully covered. This is why three audience datapoints can pin down two points of
indifference. When the assumption of full coverage is dropped, two things happen. One change
is positive from the standpoint of the analysis: an additional degree of freedom is acquired, since
the market share of the “outside option” (not watching television news) may be used in the
analysis. There are now three degrees of freedom, not two. The other change is negative from the
standpoint of the analysis: there are now four parameters to be pinned down, not two. It is still
necessary to pin down the points of indifference among the three news programs, as before. But
it is also necessary to pin down the ranges of unserved viewers on each end of the market.
12



Figure 2 illustrates this. Viewers to the left of N
xˆ do not watch news, and viewers to the
01
right of N
xˆ do not watch news. However, the data on the market share of the outside option do
30
not distinguish among these two groups.

It would be possible to pin down the point of indifference if an additional assumption
were added to the framework. For example, if it were assumed that the national news programs
have positions that leave symmetric tails of unserved viewers, then the same number of viewers
would lie to the left of N
xˆ as to the right of N
xˆ . This would reduce the number of locations to be
01
30
pinned down from four to three, a feasible task given the three available degrees of freedom. Or,
if it were assumed that the three national news programs were evenly spaced on the line, then
there would only be three locations to pin down. However, both of these assumptions are at odds
with the motivation to undertake the analysis in the first place.

While the proposed definition of a Viewpoint Diversity Index is not perfect, it does seem
better than what has been done before, since it may be objectively measured, it separates viewer
preferences from program content, and its underlying assumptions may be clearly evaluated. The
next section shows how the new index is related to ownership data.

3. Empirical Approach
This section describes the model, estimation and data used to link changes in the Viewpoint
Diversity Index to changes in media market ownership.

3.1. Model and Estimation
The model links changes in the Viewpoint Diversity Index to changes in media ownership
variables. The model is designed to fit the available data, which is characterized by the “large N,
small T” property common to many survey panel datasets.
The approach is to estimate a descriptive regression since viewpoint diversity and media
ownership may be driven by common factors. If the reader adopts the assumption that media
ownership drives viewpoint diversity, a position that has sometimes been taken by the courts,
then the empirical results may be interpreted as causal. However, the analysis here is more
cautious and does not seek to attach causal inferences to the empirical results.
13


D represents the Viewpoint Diversity Index in media market m at time t
,
1
,
0
{
}
2
mt
(corresponding to 2005, 2007 and 2009). It is constructed from the available viewing data as
presented in section 2.4. x is the vector of ownership variables; variable selection and
mt
definitions are discussed in section 3.3. It is assumed that
ln D
x
,
(14)
mt
m
t
mt
mt
where
is a market fixed effect representing characteristics that may influence the viewpoint
m
diversity provided by the media market,
is a time fixed effect,
is a parameter vector to be
t
estimated and the object of primary interest, and
captures idiosyncratic shocks that vary
mt
across markets and time periods. The log transformation is used so that parameter estimates may
be interpreted as percentage changes in the viewpoint diversity index. Equation (14) should be
thought of as a moving-average representation that likely includes serial correlation in
. If the
mt
precise form of the serial correlation were known, equation (14) could equivalently be expressed
as an auto-regressive model with lags of the dependent variable appearing as regressors on the
right-hand side.

The market-specific fixed effects in equation (14) are problematic because they are too
numerous to estimate with the available data. A random effects specification would also be
problematic, since market-specific realizations of the random effects would be correlated with
the observed ownership variables, as discussed above. Therefore, equation (14) is differenced so
that the market-specific effects drop out:

(ln D
ln D
)
(x
x
)
u ,
(15)
mt
mt 1
t
mt
mt 1
mt
where
and u
.
t
t
t 1
mt
mt
mt 1

A common approach would be to apply Ordinary Least-Squares (OLS) regression to
equation (15). This is commonly known as the “Differences-In-Differences” estimator and has
been used widely in recent years. The problem with the OLS approach is that, when serial
correlation is present in the errors, the standard errors of the parameter estimates may be severely
biased. This has been known since Cochrane and Orcutt (1949). Recently, Bertrand, Duflo and
Mullainathan (2004) explored the extent to which this issue affects policy-oriented econometric
research. They generated random treatments in their data and estimated the effects of these
“placebo laws” on female wages. They found that 45% of the placebo treatments’ parameter
14


estimates were statistically significant at the 95% confidence level. This is quite strong evidence
against OLS estimation of equation (2), and the paper instead advocates using clustered standard
errors, showing that this alternative to OLS performs about as well as nonparametric estimation
in monte carlo simulations. Therefore, the estimation allows for autocorrelation in the errors and
uses an unstructured “sandwich” estimator to control for nontemporal correlation among the
error terms, as in Arellano (1987).

A final word is in order about an estimation technique that is not used. The recent
dynamic panel estimation literature (e.g., Arellano and Bond 1991) has advocated using lags and
previous levels as instruments for future changes in variables. For example, if the error term
exhibits one-period autocorrelation, then the value of the dependent variable in period t may be
used as an instrument for the change in the error from period t 1 to period t
2 . Since only
three time periods of data are available in the present application, this necessitates throwing
away at least half of the data and some of the ownership variables. Further, it would only be
valid if the autocorrelation is of order one, an assumption that is untestable and considered
unlikely to hold.

3.2. Data
This section describes the data, ownership variables and market selection.

3.2.1. Data Description
The dataset contains information about 210 local media markets in each of three time periods
from two sources. Media ownership variables were provided by the FCC. They correspond to
three snapshots in time: December 31, 2005, December 31, 2007, and December 31, 2009.

The second dataset consists of television ratings provided by Nielsen Media Research
Galaxy ProFile. The ratings correspond to the November and May “sweeps” months in the 2005-
06, 2007-08 and 2009-10 television seasons. Nielsen selects participants through geographic
randomization and provides financial incentives to participate. In larger media markets, Nielsen
measures television viewing with PeopleMeters, which record television usage and tuning
continuously and prompt viewers to indicate their presence via remote control once or twice per
hour. In smaller markets, audimeters attached to televisions measure set usage and tuning
15


continuously. Viewer presence is measured via self-reported diaries. Nonresponsive participants
are removed from the sample quickly. Responsive participants are replaced at regular intervals.

The Nielsen data were inconsistently reported. Many datapoints and some entire market-
month datasets were missing from the data. These issues affected the variable definitions in three
ways. First, five markets (Alpena, Biloxi, Miami, New Orleans and West Palm Beach) were
dropped since a balanced panel could not be constructed for these markets. Second, because the
measurement technology is more reliable for households than for demographic groups, the
analysis focuses on household ratings. Demographic group ratings are excluded as these are
more often missing. Third, even in the household-level ratings, about 20% of the possible
observations are missing. Therefore, the local news audience share analysis focuses primarily on
evening news viewing, since this daypart featured the highest percentage of data availability
(94%) and local news programming.

The time window analyzed was 6:00-7:00 p.m. EST, 5:00-6:00 p.m. CST, 5:00-6:00 p.m.
MST and 6:00-7:00 p.m. PST. Virtually every local station in the sample airs a local newscast in
the first half-hour within this window, and airs its affiliated network’s national newscast within
the second half-hour of this window. There were a few markets, such as Spokane, in which local
newscasts did not precede national news but were aired immediately afterwards; in those markets
the time period analyzed started thirty minutes later.

Data on market-level demograhpics are used in section 4.2, including median household
income, median age, the proportion of Spanish-speaking households, the number of television
stations per capita, the percentages of households with televisions and pay-television service.
These data were collected by the American Community Survey and were provided by the FCC in
conjunction with the media ownership data. They are used to ensure consistency with other
studies in the quadrennial review. It was not clear whether the demographic variables were
defined consistently across the three snapshots in the sample, so 2007 and 2009 demographic
data are not used in the analysis.

3.2.2. Media Ownership Variables
This section defines the set of media ownership variables. Ownership variables were chosen
according to their relevance to the media ownership rules, but their number was limited to
prevent multicollinearity from inflating the standard errors of the estimates. Three ownership
16


variables were reliably measured and varied extensively, and therefore are included in the base
set of ownership variables x :
mt
Co-ownedTV: The number of television station parents that controlled more than one television
station in the same media market.
TV/Radio: The number of television stations whose parent controlled at least one radio station in
the same market.
LocalOwnerTV: The number of television stations in the market controlled by entities located
within the market.
Two additional ownership variables are available:
TV/Newspaper: The number of television stations whose parent controlled at least one newspaper
in the same market. This ownership variable exhibits the least variation. It changed in
only one market in 2005-2007, and changed in five markets in 2007-2009.
MinorityOwnerTV: The number of television stations in the market with an identifiable controller
who was a member of a minority race/ethnicity. This variable was only measured reliably
in 2007 and 2009; see Turner (2006) for further discussion.
Unfortunately, TV/Newspaper does not show meaningful variation in 2005-2007, and
MinorityOwnerTV data are not available for 2005. Therefore, these two variables must be
excluded from the base set of ownership variables. However, both can be included in a
regression based on 2007-2009 data alone. Therefore, these two variables are included in an
“augmented” set of ownership variables below.

All ownership variables are defined as count data. Percentage definitions were found to
be misleading, as they are influenced by changes in the base number of television stations in the
market. Small independent TV stations sometimes start or stop broadcasting, which then changes
all cross-ownership and co-ownership percentage variables in the market. However, because
these changes typically occur on the fringe of the TV market, they seldom indicate meaningful
changes in station ownership concentration.

Another ownership diversity variable measured the number of television stations in each
market with an identifiable controller who was female. However, the data collection
methodology for this variable indicated it was only reliably available for 2007. Since the
empirical approach relies on differences, and only a single year of data was available for this
variable, it was dropped from the analysis.
17



To summarize the ownership variables, TV/Newspaper is relevant to the
Newspaper/Broadcast Cross-Ownership Rule; Co-ownedTV is relevant to the Local TV Multiple
Ownership Rule; TV/Radio is relevant to the Local Radio/TV Cross-Ownership Rule; and
LocalOwnerTV and MinorityOwnerTV are relevant to the impact of ownership diversity on
media market competition and localism.

3.2.3. Market Selection
Since the Viewpoint Diversity Index defined in section 2 requires at least three newscasts, and
since multiple newscasts would fundamentally change the definition and implications of the
measure, market selection is an important consideration. Local media markets that did not offer
all three national broadcast networks’ news programs (ABC, CBS, NBC) and local news
programs on those network affiliates were dropped from the analysis. This narrowed the number
of markets included from 205 to 132.

Further, the Viewpoint Diversity Index will be fundamentally different in a market with a
larger number of local newscasts. FOX affiliates provided local newscasts in the evening daypart
in some markets. To gauge the sensitivity of the empirical results to the presence of a fourth local
newscast, the empirical analysis is also performed using the subsample of 99 markets in which
evening news was not available on the local FOX affiliate. This was done to gauge the sensitivity
of the results to the assumption of three local newscasts.

Third, in addition to the ABC, CBS and NBC national newscasts, Spanish-language
networks Univision and Telemundo also offer national news programs. It is unlikely that these
newscasts compete extensively with the English-language national news programs for viewers,
as most viewers are not bilingual, so they are not incorporated into the Viewpoint Diversity
Index. However, their presence in a market could potentially change the dynamics of
competition among the English-language language local newscasts. Therefore, the analysis is
repeated on the subsample of 103 markets in which fewer than 20% of self-identified heads of
household report that English is not their native language.

4. Empirical Results
This section reports the estimation results.

18


4.1. Viewpoint Diversity Index
The Viewpoint Diversity Index was fairly straightforward to calculate and displays substantial
variation across markets. Table 1 shows the raw data for 2007-2009, so that the reader may
compare the changes in the log of the Viewpoint Diversity Index to changes in the media
ownership variables.

The table is sorted in ascending order of the change in log Viewpoint Diversity. Visual
inspection shows that there is little in the way of a relationship between Viewpoint Diversity and
the media ownership variables. Extreme changes in viewpoint diversity at the high end and low
end do not coincide with unusual changes in any of the ownership variables. A similar pattern
was observed in the 2005-2007 data.

4.2. Estimation in Levels
To understand how the empirical Viewpoint Diversity Index is related to ownership variables
and demographics, equation (14) is estimated in levels. Since there are not enough degrees of
freedom to identify the market-specific fixed effects, a simplifying assumption is made that
w
, where w is a vector of market demographics and media availability and
is a
m
m
m
parameter vector to be estimated. Since this simplification does not control for market
unobservables completely, the results are provided only to understand how the Viewpoint
Diversity index correlates with market-level factors. No causal interpretation should be attached
to the resuts.

Table 2 provides the estimation in levels for the full sample and for each individual year
in the sample. The data fail to reject the null hypothesis that the parameter estimates vary across
years in the sample, so the discussion is limited to the full sample results. The media and
demographic variables explain a reasonable percentage of the variation in the Viewpoint
Diversity Index, with an R-squared statistic of .66. The only statistically significant correlation
indicates that a one-unit increase in the number of co-owned TV stations is associated with a
reduction in Viewpoint Diversity of [
%,
6
.
1
%]
1
.
0
. The 95% confidence intervals for LocalTV
and TV/Radio are similarly tight, [-0.6%, 0
.5%] and [-0.6%,1 %]
.8
, respectively.

4.3. Estimation in Differences
19


Table 3 displays the estimation in differences for the full 2005-2009 sample using the base set of
three ownership variables. The fit of the regression is very low, with time effects and changes in
ownership variables explaining just 2.4% of the changes in viewpoint diversity. None of the
effects are significant at the 95% confidence level.

The model was estimated on two special cases to gauge the sensitivity of the results to the
assumptions. One of the assumptions was that only three local newscasts were available in a
market at the dinner hour. Therefore the second column of Table 3 gives estimation results based
on a data subsample that excludes all markets in which the FOX affiliate offered local evening
news.

Another assumption is that all viewers’ preferred viewpoints can be expressed in a single
dimension of horizontal differentiation. This would not hold when an appreciable minority of
viewers prefer Spanish news. Therefore the model was estimated on another subsample, this time
based on markets in which at least 80% of consumers are native English speakers.

It appears that the results are somewhat sensitive to both assumptions. Therefore, a final
regression was run on the subsample excluding both FOX markets and markets with high
concentrations of Spanish speakers. This is the preferred specification. The point estimates
indicate that viewpoint diversity is positively associated with TV station ownership
concentration and TV/Radio cross-ownership, and negatively associated with local TV
ownership. However, none of the signs of the estimates allow the point estimates to be
statistically distinguishable from zero.

While none of the point estimates can be distinguished from zero, it can be seen that the
confidence bounds indicate the effects are not very large. The confidence interval is roughly the
point estimate plus/minus two times the standard deviation. Since the dependent variable is the
change in log viewpoint diversity, the confidence interval can be given a meaningful
interpretation. For example, the confidence interval on LocalOwnerTV in the fourth column of
Table 3 indicates that its effect is 95% likely to lie within the range [
%]
1
.
1
%,
5
.
4
. Therefore,
while this effect may be positive or negative, its absolute value is quite unlikely to be larger than
5%. The confidence intervals for Co-OwnedTV and TV/Radio are [-2.4%,3
.6%] and
[-2.3%,8 %]
.5
, respectively.

Table 4 presents the same exercise for the augmented set of ownership variables. Since
TV/Newspaper did not show meaningful variation in 2005-2007 and MinorityOwnerTV was not
20


reliably measured in 2005, this required using data for 2007-2009 only. Again, the results were
sufficiently sensitive to market selection that the preferred set of estimates excludes markets with
local FOX evening news and high concentrations of Spanish speakers. The results are a bit less
precise than when all time periods’ data are used because of the smaller sample size. The
association between viewpoint diversity and TV/Newspaper is 95% likely to lie within the range
[-1%,6%].The association between viewpoint diversity and MinorityOwnerTV is 95% likely to
lie within the range [-5%,9%].

5. Summary and Conclusions
This paper proposed a novel market-based approach to measuring Viewpoint Diversity and used
data from a panel of local media markets to investigate how it is associated with local media
ownership variables. These associations are statistically indistinguishable from zero, and all are
estimated to be less than 10% in absolute magnitude. Still, the following results may contribute
to the policy discussion on the FCC’s media ownership rules.

Newspaper/Broadcast Cross-Ownership Rule

: Based on the 2007-2009 subsample, the
correlation between viewpoint diversity and TV/Newspaper cross-ownership is 95%
likely to lie within the range [-1%,6%].

Local TV Multiple Ownership Rule

: The correlation between viewpoint diversity and TV
station ownership concentration is 95% likely to lie within the range [
%]
3
%,
2
. This
range was roughly constant between the full sample results and the 2007-2009 subsample
results.

Local Radio/TV Cross-Ownership Rule

: The empirical results indicate that the correlation
between viewpoint diversity and TV/radio cross-ownership is 95% likely to lie within the
range [
%]
9
%,
2
. This range was roughly constant between the full sample results and
the 2007-2009 subsample results.

Ownership Diversity

: The full sample results indicate that the correlation between viewpoint
diversity and local TV station ownership is 95% likely to lie within the range [
%]
1
%,
5
.
The 2007-2009 results indicate the correlation is 95% likely to lie within the range
[
%]
3
%,
2
. The 2007-2009 results indicate that the correlation between viewpoint
21


diversity and minority ownership of TV stations is 95% likely to lie within the range
[
%]
9
%,
5
.
The evidence provided in this report is intended to contribute to the policy debate around the
media ownership rules. However, it does not provide any conclusive basis for policymaking.
This paper describes statistical relationships without any claims of causality. Its findings are
limited by the range of the available data and the reader is reminded that an absence of evidence
is not evidence of absence.

Works Cited.

Arellano, M. 1987. Computing Robust Standard-Errors for Within-Group Estimators. Oxford
Bulletin of Economics and Statistics, 49,4, 431-434.
Arellano, M., S. Bond. 1991. Some Tests of Specification for Panel Data: Monte Carlo Evidence
and an Application to Employment Equations. Review of Economic Studies, 58, 2, 277-
297.
Bertrand, M., E. Duflo, S. Mullainathan. 2004. How Much Should We Trust Differences-In-
Differences Estimates? Quarterly Journal of Economics, 119,1, 249-275.
Cochrane, D., G. H. Orcutt. 1949. Application of Least-Squares Regression to Relationships
Containing Auto-Correlated Error Terms. Journal of the American Statistical
Association, 44, 245, 32-61.
Gentzkow, M., J. M. Shapiro. 2010. What Drives Media Slant? Evidence from U.S. Daily
Newspapers. Econometrica, 78, 1, 35-71.
Hotelling, H. 1929. Stability in Competition. Economic Journal, 39, 41-57.
McCann, K. 2010. A Diversity Policy Model & Assessment: Debates and Challenges of [Media]
Diversity. Working paper, Fordham University.
Turner, S. D. 2006. Out of the Picture: Minority & Female TV Station Ownership in the United
Stateshttp://www.freepress.net/files/out_of_the_picture.pdf">. http://www.freepress.net/files/out_of_the_picture.pdf, accessed March 2011.
US Federal Communications Commission. 2010. Notice of Inquiry.http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-10-92A1.pdf"> http://hraunfoss.fcc.gov/
http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-10-92A1.pdf">edocs_public/attachmatch/FCC-10-92A1.pdf, accessed March 2011.


22



Table 1. Raw Data fo0072 2007-2009 Subsample


y
t

y
y
i
t
t
s
i
i
r
s
s
e
r
r
v
e
e
i
V
v
i

V
v
i

V
T
T
T
D
r
V
r
D
r
V
r
D
r
V
r
nt
T
V
nt
T
V
nt
T
V
wne
r
r
r
dT
wne
wne
pape
dT
pape
dT
pape
wpoi
O
o
O
o
O
o
e
y
wne
wpoi
y
wne
wpoi
y
wne
i
t
i

t
t
O
wne
ws
e
i

i
O
wne
ws
e
i

i
O
wne
ws
V
adi
adi
adi
al
R
V
V
/
Ne
/

al
R
/

Ne
/

al
R
/

Ne
/

og
nor
nor
nor
oc
V
V
og
oc
V
V
og
oc
V
V
Mi
L
Co-O
T
T
Mi
L
Co-O
T
T
Mi
L
Co-O
T
T
n L
n
n
n
n
n
n L
n
n
n
n
n
n L
n
n
n
n
n
Television M arket
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Television M arket
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Television M arket
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Chg. i
Juneau, AK
-0.19
0
0
0
0
0
Waco-et al.
-0.02
0
-1
0
0
-1
Chico-Redding, CA
0.02
0
0
0
0
0
Anchorage, AK
-0.14
0
0
0
0
0
Columbus, GA
-0.02
0
0
0
0
0
Norfolk-et al.
0.02
0
1
0
0
0
Harlingen-et al.
-0.13
0
0
0
0
0
Washington, DC
-0.02
0
0
0
0
0
Atlanta, GA
0.02
0
0
0
0
0
Madison, WI
-0.13
0
0
0
0
0
Dayton, OH
-0.01
0
0
0
0
0
Detroit, MI
0.02
0
0
0
0
0
San Angelo, T X
-0.11
1
-1
0
0
0
Charleston-et al.
-0.01
0
0
0
0
0
Hartford-et al.
0.02
0
0
0
0
0
T ampa-et al.
-0.11
0
0
0
0
0
Jackson, MS
-0.01
0
0
0
0
0
Charlotte, NC
0.02
0
0
0
0
0
Denver, CO
-0.09
0
0
0
-1
0
Johnstown-et al.
-0.01
0
0
0
0
0
Sacramento-et al.
0.02
0
0
0
0
0
Spokane, WA
-0.08
0
0
0
0
0
Los Angeles, CA
-0.01
1
0
0
1
0
Albany-et al.
0.02
0
0
0
0
0
Bangor, ME
-0.08
0
0
0
0
0
Shreveport, LA
-0.01
0
0
1
0
0
Memphis, T N
0.02
0
0
0
0
0
Meridian, MS
-0.07
0
0
0
0
0
Greenville,SC-et al.
-0.01
0
0
0
0
0
Kansas City
0.02
0
0
0
0
0
Charlottesville, VA
-0.07
0
0
0
0
0
Montgomery, AL
-0.01
0
1
0
0
0
Monroe-et al.
0.02
0
-1
0
0
0
La Crosse-et al.
-0.07
0
0
0
0
0
Minneapolis - et al.
-0.01
0
0
0
0
0
Minot-et al.
0.02
0
0
0
0
0
Peoria-et al.
-0.07
0
0
0
0
0
Lexington, KY
0.00
0
-1
0
0
0
Duluth-et al.
0.02
0
0
0
0
0
Savannah, GA
-0.07
0
0
0
0
0
Oklahoma City, OK
0.00
0
1
1
0
0
Columbia-et al.
0.03
0
0
0
1
0
Las Vegas, NV
-0.06
0
0
-1
0
0
Knoxville, T N
0.00
0
0
0
-1
0
Mobile, et al.
0.03
0
0
0
0
0
Sioux Falls-et al.
-0.06
0
0
0
0
0
Baton Rouge, LA
0.00
0
0
0
0
0
Springfield-et al.
0.03
0
0
0
0
0
Paducah-et al.
-0.05
1
0
0
0
0
Youngstown, OH
0.00
0
0
2
0
0
Raleigh-et al.
0.03
0
0
0
0
0
Lansing, MI
-0.04
1
0
0
0
0
Chicago, IL
0.00
0
0
0
0
0
Springfield, MO
0.03
0
-1
1
0
0
Fresno-Visalia, CA
-0.04
0
-2
0
0
0
T allahassee-et al.
0.00
0
0
0
0
0
Abilene-et al.
0.04
1
0
0
0
0
Birmingham, AL
-0.04
0
0
0
0
0
Joplin, et al.
0.00
0
0
0
0
0
Dallas-et al.
0.04
0
0
1
0
1
Charleston, SC
-0.04
0
0
0
0
0
Columbia, SC
0.00
0
0
0
0
0
Odessa-et al.
0.04
0
0
0
1
0
Columbus, OH
-0.04
0
-1
0
0
0
Little Rock-et al.
0.00
0
-2
1
0
0
Green Bay-et al.
0.04
0
0
0
0
0
Huntsville-et al.
-0.04
0
0
0
0
0
Augusta, GA
0.00
0
0
0
0
0
Ft. Wayne, IN
0.04
1
0
0
0
0
Houston, T X
-0.04
0
-1
0
0
0
Amarillo, T X
0.00
0
0
1
0
0
Omaha, NE
0.04
0
0
-1
0
0
Pittsburgh, PA
-0.04
0
0
0
0
0
Columbus-et al.
0.00
0
0
0
0
0
Evansville, IN
0.04
0
0
0
0
0
Louisville, KY
-0.04
0
0
0
0
0
South Bend-et al.
0.01
0
0
0
0
0
Ft. Smith-et al.
0.04
0
0
1
0
0
Idaho Falls-et al.
-0.04
0
0
0
0
0
Greenville-et al.
0.01
0
0
0
0
0
Wilkes Barre-et al.
0.05
0
0
0
0
0
Fargo, ND-et al.
-0.04
0
0
0
0
0
Syracuse, NY
0.01
0
0
0
0
0
T ulsa, OK
0.05
0
0
0
0
0
Binghamton, NY
-0.04
0
0
0
0
0
Buffalo, NY
0.01
0
0
0
0
0
New York, NY
0.05
0
1
0
0
-1
Cedar Rapids-et al.
-0.03
0
0
1
0
0
Ft. Myers-et al.
0.01
0
0
0
0
0
T yler-Longview, T X 0.05
0
0
1
0
0
Burlington, VT -et al. -0.03
0
0
0
0
0
Milwaukee, WI
0.01
0
0
0
0
0
T ucson, AZ
0.05
0
0
1
0
0
Corpus Christi, T X
-0.03
0
1
0
0
0
Harrisburg-et al.
0.01
0
0
0
0
0
Greensboro-et al.
0.06
1
0
0
0
0
T raverse City-et al.
-0.03
1
0
0
0
0
Flint-et al.
0.01
1
0
0
0
0
St. Louis, MO
0.06
0
0
1
-1
0
Phoenix, AZ
-0.03
1
0
0
0
0
Nashville, T N
0.01
0
0
0
0
0
Philadelphia, PA
0.06
1
-1
0
-1
0
Baltimore, MD
-0.03
0
0
0
0
0
San Francisco-et al.
0.01
-1
0
0
-1
0
Rockford, IL
0.07
0
0
0
0
0
T opeka, KS
-0.03
0
0
0
0
0
Jacksonville, FL
0.01
0
0
-1
0
0
Santa Barbara-et al.
0.08
0
0
0
0
0
Wichita - et al.
-0.03
0
0
0
0
0
T ri-Cities, T N-VA
0.01
0
0
0
0
0
Bluefield-et al.
0.08
0
0
0
0
0
Des Moines-et al.
-0.03
0
0
0
0
0
Portland-Auburn
0.01
0
0
1
0
0
Austin, T X
0.09
0
0
0
0
0
Providence-et al.
-0.02
0
0
0
0
0
Cincinnati, OH
0.01
0
0
0
0
-1
Lubbock, TX
0.09
0
0
0
0
0
Macon, GA
-0.02
0
0
0
0
0
San Antonio, T X
0.01
0
0
0
0
0
Salt Lake City, UT
0.09
0
0
0
0
0
Orlando-et al.
-0.02
0
0
0
0
0
Wichita Falls, et al.
0.01
0
0
0
0
0
T oledo, OH
0.10
0
0
0
0
0
Indianapolis, IN
-0.02
0
0
0
0
0
Davenport, IA-et al.
0.02
0
0
0
0
0
Marquette, MI
0.12
0
0
0
0
0
Rochester, NY
-0.02
0
0
0
0
0
Champaign-et al.
0.02
0
0
0
0
0
Medford-et al.
0.19
0
0
0
0
0
Grand Rapids-et al.
-0.02
1
0
0
0
0
Roanoke-et al.
0.02
0
-1
1
0
0
Boston, MA
0.27
-1
0
0
0
0



23


Table 2. Estimation in Levels

2005-2009
2005 only
2007 only
2009 only

Point

Std.

Point

Std.

Point

Std.

Point

Std.

Media Ownership

Est.

Err.

Est.

Err.

Est.

Err.

Est.

Err.

Median age
.001 (.002)
.000 (.003)
.000 (.002)
.003 (.002)
Median income
.000 (.000)
.000 (.000)
.000 (.000)
.000 (.000)
Spanish-speaking population
.014 (.034)
.039 (.040)
.008 (.062)
-.004 (.051)
Local evening FOX news
.011 (.011)
-.004 (.012)
.010 (.016)
.027 (.015)
TV channels per capita
-.001 (.001)
-.003 (.003)
-.001 (.002)
-.001 (.002)
Pay TV penetration
-.089 (.096)
-.073 (.104)
-.175 (.126)
.015 (.149)
TV penetration
.084 (.070)
.087 (.150)
.220 (.108) *
.011 (.079)
LocalOwnerTV
-.001 (.003)
-.001 (.003)
-.002 (.004)
.000 (.004)
Co-OwnedTV
-.008 (.004) *
-.004 (.005)
-.014 (.007) *
-.008 (.005)
TV/Radio
.006 (.006)
.005 (.007)
.007 (.009)
.009 (.008)
Year 2005 Intercept
-.103 (.089)
-.045 (.123)
Year 2007 Intercept
-.092 (.091)
-.111 (.164)
Year 2009 Intercept
-.092 (.092)
-.188 (.131)
Num. Obs.
396
132
132
132
R-squared
.655
.713
.641
.666
* Significant at the 95% confidence level.


Table 3. Estimation in Differences, 2005-2009 Data

2005-2009,
2005-2009,
2005-2009,

No FOX or

2005-2009,

No FOX

No Spanish

Spanish

All Markets

Markets

Markets

Markets

Point

Std.

Point

Std.

Point

Std.

Point

Std.

Media Ownership

Est.

Err.

Est.

Err.

Est.

Err.

Est.

Err.

Change in LocalOwnerTV
-.013 (.015)
-.003 (.014)
-.027 (.016)
-.017 (.014)
Change in Co-ownedTV
.009 (.014)
.002 (.015)
.005 (.014)
.006 (.015)
Change in TV/Radio
.026 (.017)
.029 (.022)
.024 (.020)
.031 (.027)
Year 2007 Intercept
.026 (.013) *
.022 (.015)
.030 (.013) *
.028 (.016)
Year 2009 Intercept
-.004 (.011)
-.011 (.012)
-.003 (.011)
.010 (.012)
Num. Obs.
264
198
238
176
R-squared
.0242
.020
.037
.033
* Significant at the 95% confidence level.



24



Table 4. Estimation in Differences, 2007-2009 Data with Additional Ownership Variables

2007-2009,
2007-2009,
2007-2009,

No FOX or

2007-2009,

No FOX

No Spanish

Spanish

All Markets

Markets

Markets

Markets

Point

Std.

Point

Std.

Point

Std.

Point

Std.

Media Ownership

Est.

Err.

Est.

Err.

Est.

Err.

Est.

Err.

Change in LocalOwnerTV
.013 (.013)
.013 (.015)
.001 (.015)
.007 (.015)
Change in Co-ownedTV
.030 (.018)
.019 (.017)
.025 (.018)
.012 (.016)
Change in TV/Radio
.014 (.040)
.042 (.023)
.001 (.047)
.029 (.026)
Change in MinorityOwnerTV
-.047 (.049)
-.017 (.037)
.038 (.054)
.019 (.036)
Change in TV/Newspaper
-.015 (.027)
.005 (.018)
.015 (.032)
.023 (.018)
Year 2009 Intercept
-.001 (.013)
-.011 (.013)
.002 (.013)
.012 (.013)
Num. Obs.
132
99
119
88
R-squared
.029
.022
.019
.015
* Significant at the 95% confidence level.



25



Figure 1. Diversity Index Example

Dm
Market m
m
xˆ
m
xˆ
12
23
m
x
m
x
m
x
1
2
3
Market m'
'
ˆm
'
x
ˆm
x
12
23
m'
x
m'
x
m'
x
1
2
3
Dm'



Figure 2. Uncovered Media Market

watching
watching
watching
watching
watching
no news
news 1
news 2
news 3
no news
N
N
xˆ01
xˆ
N
xˆ
N
xˆ
12
23
30
N
x
N
x
N
x
1
2
3


26


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