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Media Ownership Study 1-Submitted Study

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Released: July 21, 2011

Local Media Ownership and Media Quality

April 5, 2011

Adam D. Rennhoff
Assistant Professor, Jones College of Business
Middle Tennessee State University
P.O. Box 27
Murfreesboro, TN 37132

Kenneth C. Wilbur
Assistant Professor, Fuqua School of Business
Duke University
100 Fuqua Drive, Box 90120
Durham, North Carolina 27708-0210

We thank the Federal Communications Commission for providing data, and Jonathan Levy and
Tracy Waldon for many helpful comments and discussions. Any remaining errors are our own.

Executive Summary

The United States Federal Communications Commission regulates local media ownership to
promote competition, diversity and the provision of local programming. This study investigates
how local media cross-ownership, co-ownership and ownership diversity are associated with
media market outcomes. It does so by regressing changes in local-market media quality variables
on changes in local-market media ownership variables. Little robust evidence is found to indicate
that local media ownership affects local media usage or programming.

1. Introduction
This study was commissioned by the United States Federal Communications Commission
("FCC") as part of its 2010 Quadrennial Review of Media Ownership Rules. The study analyzes
media usage and ownership data to determine how changes in local media market ownership
structure are associated with changes in local media market outcomes, mainly from the
standpoint of competition and the provision of local programming.

Section 1.1 explains the relevant rules that the FCC is reviewing and section 1.2 discusses
the relevant academic literature. Section 2 describes the conceptual background for the analysis,
the technical details, and the data. Section 3 presents the results and section 4 relates the results
to the specific media ownership rules to be reviewed.

The FCC's policy goals for its Review of Media Ownership Rules are competition,
localism and ownership diversity. The current analysis will produce three sets of descriptive
results that are relevant to these goals. First, it will examine how media cross-ownership and co-
ownership are associated with media competition from a consumer perspective, as measured by
media usage. Second, it will determine how media cross-ownership and co-ownership are
associated with localism, as measured by the amount of local news provided in the market. The
term "media quality" is used to refer to the collection of variables measuring media usage and
local news provision. Third, it will determine how media ownership diversity, as measured by
station ownership characteristics, is related to media quality.

1.1. Regulatory Background
Three media ownership rules are relevant to the present analysis. This section gives just a brief
overview of the rules. FCC (2010) provides a complete explanation.

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. 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 multiple 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 multiple station ownership in smaller markets, as they are typically served by fewer
than eight stations.

Local Radio/TV Cross-Ownership Rule

: The application of this rule depends on the number of
"voices"--TV stations, radio stations, newspapers and a cable system--in a media
market. 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 smaller
markets, an entity that owns a TV station may not own more than one radio station.

Among these rules, the Newspaper/Broadcast Cross-Ownership Rule is of particular
interest. Newspaper advertising revenues (print and online) fell 44% in the three years ended
December 31, 2009, and circulation revenues fell 6% over the same period. These changes led
newspapers to reduce their professional editorial staff by 25%. The situation continues to
worsen, as newspapers were the only medium in which ad revenue fell in 2010 (PEJ 2011).

These changes are troubling from a policy perspective, as newspaper readership has been
associated with increased voter information and participation. Historically, the entry of a
newspaper into local markets increased voter turnout (Gentzkow, Shapiro and Sinkinson,
forthcoming). Subsequent television station entry into local markets correlated with sharp drops

in both newspaper consumption and voter turnout (Gentzkow 2011). Survey evidence suggests
that knowledge of local politics drops during newspaper strikes (Mondak 1995). Taken together,
these findings suggest that newspapers have played a special role in informing the public about
local politics.

This special role may come from fundamental differences in the way that television and
newspaper media present the news, since television faces tighter time constraints and often
requires supplementary video. Milburn and McGrail (2001) argued that television news
presentation typically follows a dramatic narrative arc. A controlled experiment showed that
dramatic presentation simplifies consumer thinking and reduces recall of news stories. Chiricos,
Eschholz, and Gertz (1997) found that an individual's fear of crime is positively correlated with
their television and radio news consumption but not with newspaper or news magazine

Further, newspapers and television differ in the range of news stories they cover. Baldwin
et al. (2010) found that local newspapers provided far more coverage of city government than
local television stations. Semetko and Valkenburg (2001) found that newspapers devoted a
higher proportion of coverage to crime and education stories than television news programs. TV
news reported proportionally more foreign affairs and "human interest" stories.

It seems possible that allowing mergers between newspapers and television stations could
lead to substantial economies of scope and may improve product offerings by enabling cross-
media promotions and integrated delivery. Many newspapers now offer some video content
online, and many television stations' websites provide large repositories of text news stories. To
better understand the potential consequences of such mergers, however, it is necessary to discern
how they correlate with local news availability and quality.

1.2. Literature Review
Academic research on the impact of media ownership on media market outcomes goes back at
least to Steiner (1952). Steiner showed that commonality in viewer preferences regarding
program types can result in a tyranny of the majority in program provision. When there are two
broadcasters and two program types, one of which is preferred by more than two-thirds of the
viewing population, both broadcasters will air a program of the popular type. Attracting half of
the larger audience is more profitable than attracting all of the smaller audience, so the less

popular program is not offered. A two-channel monopoly can deliver better results than duopoly
because it airs both programs and serves the entire market. Steiner argued that the degree to
which this result holds in a multi-period model with competing broadcasters depends on the
"shiftability" of consumer preferences--that is, the extent to which media consumers prefer
certain programs at certain times, such as news in the morning, or entertainment after dinner.

Modern treatments of media markets recognize that media outlets serve multiple groups
of customers, including viewers, advertisers and content producers. This framework is called
"multi-sided platform industries" (or "two-sided markets"), and its key insight is that user
charges reflect both the cost of platform provision and the effect of the agent's platform usage on
agents of other types. The pioneering treatment in this area was Anderson and Coate (2005),
whose model captured the nonrival nature of television program consumption by viewers, the
influence of viewers on advertising revenues, and the negative impact of advertising sales on
audiences. These authors predicted that ownership consolidation raises media and advertiser
profits at the expense of consumer welfare, with an ambiguous effect on social welfare. This
occurs because media consolidation reduces competition for viewers and increases the amount of
advertising carried by the media.

The subsequent theoretical literature pushed the bounds of the Anderson-Coate
framework in a number of directions. The following discussion is limited only to those papers
that directly examine the effects of entry or consolidation on competition, and the findings are
discussed in the context of the television industry, although most of these models could be
applied to any mass media industry. Crampes, Haritchabalet and Jullien (2009) showed that, with
endogenous program quality decisions, free entry may lead to a suboptimally high number of
media outlets, as program development costs are inefficiently spread across a larger number of
media outlets. Cunningham and Alexander (2004), in contrast, found that greater concentration
among media may either increase or reduce the amount of programming time served to
consumers, depending on the elasticity of viewing in response to advertising time. Dukes (2006)
endogenizes advertiser competition in the product market and finds, counterintuitively, that
advertisers may be better off with greater media concentration, since this may lead to an
equilibrium where their messages are more dispersed, softening price competition. Gal-Or and
Dukes (2006) investigate mergers among media stations, and find, in contrast with standard
results in product market oligopoly, nonconsolidating media mergers become increasingly

profitable as media concentration falls. Gentzkow and Shapiro (2008) reviewed a large body of
literature and argued that media competition depends crucially on the number of consumers who
receive news from multiple sources, as this constrains the degree to which news media compete
in the marketplace of ideas. Kind, Nilssen and Sorgard (2009) consider how two types of
competition influence media outlets' business models. They found that media content
differentiation leads to greater reliance on advertising-supported models, while a greater number
of media outlets encourages subscription pricing. Reisinger, Ressner and Schmidtke (2009)
model TV stations which compete for advertisers as well as viewers. Their model overturns
many of the previous literature's results, finding, for example, media profits and advertising
levels can actually rise with the number of independent media outlets.

To summarize the theoretical literature, competition may either increase or decrease
media market economic performance, depending primarily on (1) differentiation among media
platforms, (2) the number of media platforms, (3) the rate at which viewers switch media in
response to advertising, (4) the elasticity of advertiser demand, and (5) the degree to which
platforms compete for advertisers. Regrettably, the literature contains many opposing
predictions, so general conclusions are not available.

There has been a limited amount of empirical work related to the questions of interest in
this study. Perhaps closest is Brown and Alexander (2005), who used 1952 TV station license
allocations as an instrument to identify the effect of media ownership consolidation on station
ratings and ad prices. The identifying assumption was that the number of station licenses granted
in 1952 was likely to be correlated with media market structure in 1998 but independent of
unobserved determinants of ratings and advertising in 1998. Using a system of equations
estimated on a cross section of US media markets, they found that a 20% increase in
concentration raises advertising price by 9% and ratings by 0.8%.

Two empirical studies of the newspaper industry are related to the questions posed here.
Chandra and Collard-Wexler (2009) examined data from a four-year merger wave in which 75%
of Canadian newspapers changed hands. They found that newspaper prices rose substantially
during this period, but subscription price rises were no higher at acquired newspapers than at
non-acquired newspapers. Argentesi and Fillistrucchi (2009) proposed a structural framework to
estimate reader responsiveness to newspaper cover price and advertising quantity on one side,
and advertiser responsiveness to advertising price and circulation figures on the other. They used

their estimates to infer Italian newspaper markups under a variety of competition/collusion
assumptions. Comparing their results to markup data in newspaper financial reports, they found
evidence consistent with collusive behavior on cover prices and competitive behavior on
advertising prices.

Another body of relevant literature is the research commissioned by the FCC during its
previous ownership reviews. Most closely related is Shiman et al. (2007), who estimated a panel
regression controlling for market, affiliated network and time-specific factors with three-way
fixed effects. They found that television stations that are cross-owned with newspapers or radio
stations provided more news than other stations, but other ownership variables did not have any
impact on news provision. The main differences in the current analysis are the range of outcomes
considered and a reliance on changes in media ownership variables to identify their associations
with competition and localism.

2. Research Methodology
This section describes the research design, the empirical approach and the data.

2.1. Research Design
Three observations guided the research design.

First, the usage of each station in a market depends on the programming of all stations in
that market, and the programming of each station in the market depends on the ownership of all
stations in the market, as was explored in depth in section 1.2. This observation leads logically to
a data analysis done at the level of the media market rather than the individual media outlet,
since individual media outlets' choices are made interdependent by market competition.

Second, it is exceedingly difficult to disentangle media market ownership from media
market competition and localism. Ownership decisions may be made in anticipation of long-run
trends in media supply or demand that are observable to the station owners but not in the
available data. This suggests a possible correlation in the media ownership variables and the
residuals in any regression, a problem that has no clear solution. Therefore, this study is purely
descriptive; it makes no claims of causality. Causal interpretations of the empirical results would
need to rely on the assumption that media ownership variables are determined prior to media
quality variables. An alternate research design would be to rely on an instrumental-variables

approach. A candidate instrument would have to be correlated with the media outlet's market
share but uncorrelated with its profits, since station ownership decisions are likely based on
station profits. However, since profits typically rely on market share, finding such an instrument
is a challenging task.

Third, purely cross-sectional or purely intertemporal analyses may produce misleading
results, so panel analysis is required. Cross-sectional regressions risk spurious findings because
unobserved variables may influence both media ownership and media quality. For example,
George and Waldfogel (2003) found that newspapers in cities with larger concentrations of
black, white or Hispanic consumers have higher proportional readerships among these groups
relative to cities with less minority representation. The implication is that newspaper content is
responsive to local demographic composition. This market characteristic may be imperfectly
measured using traditional demographic statistics, but may be correlated with both media market
structure and media usage. A cross-sectional regression might find a spurious correlation
between media ownership and media quality due entirely to the dependence of each variable on
cultural homogeneity. Only a panel analysis can rule out spurious correlation due to market-
specific factors.

2.2. Empirical Approach
The analysis undertaken here regresses a vector of local market media quality variables on a set
of local media ownership variables and exogenous controls. 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.

2.2.1. Model
This section presents the model. q
y represents quality variable q in market m
1 ...,M in time
2 (corresponding to 2005, 2007 and 2009). The vector of quality variables is y .
Similarly, x represents a vector of media ownership variables. Variable selection and
definitions are discussed in section 2.3.

Equation (1) is used to predict media quality variable q,

represents all market characteristics that may influence media quality,
is a time
fixed effect,
is a parameter vector to be estimated and the object of primary interest, and q
captures idiosyncratic shocks that vary across markets, time periods and quality variables. Media
usage is typically thought to be influenced by long-term habit formation, so equation (1) should
be thought of as a moving-average representation that likely includes serial correlation in q . If
the precise form of the serial correlation were known, equation (1) 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 (1) 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 (1) is differenced so
the market-specific effects drop out:

( q
( q
) ,
mt 1
mt 1
mt 1
. The next section discusses a variety of approaches to estimate
t 1
equation (2).

2.2.2. Estimation
Three sets of estimates are presented in section 3. A common approach would be to apply
Ordinary Least-Squares (OLS) regression to equation (2). 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 estimates were
statistically significant at the 95% confidence level. This is quite strong evidence against OLS
estimation of equation (2). Yet while OLS is not viewed as a desirable model in the current
setting, it is presented in section 3 to provide a familiar benchmark.

Bertrand, Duflo and Mullainathan (2004, IV.E) advocate using clustered standard errors,
showing that this alternative to OLS performs about as well as nonparametric estimation in
monte carlo simulations. The second set of estimates presented below follows this advice. This
allows for autocorrelation in the errors and uses an unstructured "sandwich" estimator to control
for possible correlation among the error terms, as in Arellano (1987).

While the previous approach is feasible, it does not exploit the available information on
how error terms may be linked across media markets or quality variables. Therefore, the
preferred approach is to allow for the errors to be correlated across media markets, quality
variables, and time periods. This is implemented using the multi-way clustering approach of
Cameron, Gelbach and Miller (2011).

A final word is in order about one 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. Further, it would only be valid if the autocorrelation is of order
one, an assumption that is untestable and considered unlikely to hold.

2.3. Data
This section describes the dataset, variables and definitions.

2.3.1. Data Sources, Markets, Time Periods, and Exogenous Controls
The dataset contains information about 210 local media markets in each of three time periods
from two sources. Media ownership variables and market demographic variables were provided
by the FCC. Media ownership variables 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
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 analysis focuses on four-week average ratings within the
evening news daypart. The four-week average ratings are available in over 94% of the possible
observations, making them the most reliable source of information in the data.

Nielsen's data reporting methodology remains less than fully clear, despite repeated
inquiries and careful scrutiny of all available documentation. It is thought that the five
geographic markets were missing for exogenous technical reasons. Further, it is assumed that
Nielsen does not report station ratings when the number of people using television in its local
sample is relatively low, since it would be difficult to reliably estimate stations' viewing shares
based on a small number of viewers. The frequency of data availability (that is, the frequency
with which data were not missing) was roughly constant across weekdays but slightly higher for
smaller markets than for larger markets. It appeared that data availability was driven more by
variation in Nielsen's sample sizes across media markets rather than by variation in television
usage over time within a market.

2.3.2. Media Quality Variables
This section defines the set of media quality variables, y . Quality variables were chosen
according to their relevance to the FCC's policy goals and the reliability with which they could
be measured. The quality variables are:

LocalEveningRating: The average percentage of households in a market watching any local
station between the hours of 5-7 p.m. EST, 4-6 p.m. CST, 4-6 p.m. MST, or 5-7 p.m.
PST. This is the daypart with the highest coincidence of local programming and ratings
data. Since syndicated programming is typically available during this daypart, it measures
the degree to which the entire television viewing market is served, not just the segment
interested in local news.
LocalNewsHours: The number of hours of local news offered on all TV stations in the market.
LocalNewsRating: The average rating for all local news programs whose ratings are observed.
NewspaperCirculation: The estimated number of daily newspaper copies per capita distributed in
the market over the course of one week.
RadioNewsStations: The number of radio stations in the market classified as "News" format.
Values are expressed in per capita terms to correct for the common occurrence that more
populous markets are assigned more station licenses, and therefore would naturally have
more radio stations. As in Berry and Waldfogel (2001), counts of stations by format are
used because (a) data on stations' listenership were not available and (b) the number of
stations supported should be a measure of usage as well as availability, since radio
stations may easily switch formats if listeners do not patronize news stations. An
additional variable, the count of stations classified as "News/Talk" format, was
considered but produced results which were qualitatively identical to RadioNewsStations,
so this variable was dropped to simplify the exposition.
To summarize, LocalEveningRating addresses the FCC's competition goal; LocalNewsHours
addresses the FCC's localism goal; and LocalNewsRatings, NewspaperCirculation and
RadioNewsStations address both the competition and localism goals.

2.3.3. 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
variables were reliably measured and varied extensively, and therefore are included in the base
set of ownership variables w :

Co-ownedTV: The number of television station parents that control more than one television
station in the same media market.
TV/Radio: The number of television stations whose parent controls 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 controls 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.

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. Empirical Results
This section presents the estimation results. First, raw correlations are discussed. Then, the full
sample is used to estimate parameters for the base set of ownership variables. Finally, the second
half of the sample is used to estimate parameters for the augmented set of ownership variables.

3.1. Correlations
Table 1 presents raw correlations between the changes in ownership variables and the changes in
media quality variables. Two features of the correlations are notable. First, none of the
correlations is particularly large and none are significant at the 95% confidence level. This
suggests that perhaps the media ownership variables do not exert a very strong influence on the
media quality variables. Second, the correlations in 2005-2007 differ substantially from the
correlations in 2007-2009. For example, increases in co-ownership of television stations are
negatively correlated with evening television ratings (-.09) in the first half of the sample, but this
correlation is positive (.01) in the second half of the sample. Many pairwise correlations show
similar differences in sign and magnitude between the two halves of the sample. This pattern
suggests that both sets of variables may be driven by common factors, such as time effects, and
motivates the use of regression analysis.

3.2. Results: Base Ownership Variables, Full Sample
Table 2 reports estimation results for the base set of ownership variables in the full sample. The
regressions explained about 28% of the variance in the quality variables, a reasonable figure for a
difference-based regression like this one. The three columns of estimation results correspond to
the three sets of assumptions about the error covariance matrix presented in section 2.2.2. The
preferred set of estimation results are given in the third column, since this uses information on
market, quality variable and time period to add structure to the error covariance matrix.

The first thing to notice is that the point estimates are virtually unchanged under all
estimation techniques. The only changes occur in the standard errors.

Second, only two parameter estimates are statistically significantly different from zero.
Increases in local ownership of television stations are associated with reductions in the number
of news radio stations in the market and reductions in the amount of local television news
provided in the market. Further interpretation of these results is given in section 4.

3.3. Results: All Ownership Variables, Limited Sample
Table 3 reports model estimation results for the augmented set of ownership variables based on
the second half of the sample. The estimation was limited to the 2007-2009 changes because
TV/Newspaper showed almost no variation in 2005-2007 and because MinorityOwnerTV was not
available for 2005. The preferred estimates are shown in the third column of the table.

Again, the point estimates are virtually unchanged across models. However, this time the
regressions explained about 42% of the variance in the media quality variables, substantially
more than in Table 2. Since the sample size fell by half, this must be because the new ownership
variables added a substantial amount of information. Not only did the overall model fit rise, but
many parameters which were imprecisely estimated in the full sample were precisely estimated
in the restricted sample. These changes in parameter estimates were to be expected, as the
changes in media ownership variables are correlated among themselves, and the 2005-2007
correlations were observed to differ markedly from the 2007-2009 correlations.

Still, it is important to remember that these two sets of estimates have different
interpretations, so they are not directly comparable. The estimates in Table 2 show the effects
based on the full sample, whereas estimates in Table 3 show the effects based on the 2007-2009
data alone.

The results indicate that changes in MinorityOwnerTV are associated with increases in the
hours of local TV news available in a market, but they are not associated with any other effects.
There are no significant correlations between TV/Newspaper and any quality variables, as one
might expect with only five nonzero changes in TV/Newspaper in the sample.

Further, when these two ownership variables are added to the analysis, some of the
qualitative conclusions on previously considered ownership variables change. For example,
Table 3 shows that the number of news radio stations in a market is significantly positively
related to TV/radio cross-ownership and negatively related to increases in TV station ownership
concentration; both parameter estimates are insignificant in Table 2. Further, local ownership of

TV stations is not found to have any significant effects, but previously it had two significant

It is striking to note that no parameter estimate is statistically significant in both the full
dataset in Table 2 and the 2007-2009 subsample in Table 3. This suggests a lack of consistent
directional relationships between individual ownership variables and media quality variables.

4. Summary and Conclusions
This paper investigated a panel of local media markets to present evidence on how changes in
media ownership variables are correlated with changes in media quality variables. The general
lesson from this analysis is that there is no clear evidence that changes in local media ownership
produce large changes in media competition or localism. Still, the following results may
contribute to the policy discussion on the FCC's media ownership rules and media ownership
diversity policies.

Newspaper/Broadcast Cross-Ownership Rule

: The point estimates in Table 3 indicate that
newspaper-broadcast cross-ownership is positively associated with radio news
availability and local TV news provision, and negatively associated with newspaper
circulation, average local TV news ratings and aggregate evening TV ratings. However,
there were only a few changes in this variable during the sample, so none of these
directional effects can be distinguished from random noise. The lack of
television/newspaper integration since the Newspaper/Broadcast Cross-Ownership Rule
waiver criteria were loosened in 2007 leads the authors to question the economic basis for
keeping the rule in place, given the influence of newspapers on voter information and
turnout, the recent declines in newspaper revenues and news production expenditures,
and the potential economies of scope available to joint owners of news outlets in multiple

Local TV Multiple Ownership Rule

: Television station ownership concentration was
negatively related to radio news availability and local TV news provision in 2007-2009.
No other evidence is found that television station ownership concentration is associated
with media competition or localism. It is worth noting that the rule in place limited the
amount of television station concentration that could be observed in the data, as no entity
is permitted to control two large stations in a single market. The authors would hesitate to

extrapolate from these results beyond the range of TV station ownership concentration
observed in the data. Significant loosening of the rules may produce fundamentally
different patterns in the data. This is an area in which some experimentation may be an
advisable policy.

Local Radio/TV Cross-Ownership Rule

: Increases in television/radio cross-ownership were
positively associated with radio news availability and average TV news viewership and
negatively associated with local TV news provision in 2007-2009. Radio/TV cross-
ownership had no significant effects in the full sample.

Ownership Diversity

: Minority ownership of local television stations was positively related to
the number of hours of local news provided in 2007-2009. Mixed results are found with
regards to local television station ownership. In the full sample, this variable is associated
with reductions in news radio station formats and hours of local TV news provided, but in
the 2007-2009 subsample, it is not found to have a significant association with any of the
quality variables.
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.

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Table 1. Correlations

Change in Media Quality Variables

Change in Media



Newspaper RadioNews LocalNews LocalNews Evening



Full Sample (410 obs.)

2005-2007 only (205 obs.)
2007-2009 only (205 obs.)


Table 2. Estimation Results: Base Ownership Variables, Full Sample





Clustered S.E.




Point Std.

Point Std.

Qual. Media Ownership



Est. Err.

Est. Err.

Change in NewspaperCirculation
Change in Co-ownedTV
.004 (.432)
.004 (.005)
.004 (.004)
Change in TV/Radio
.010 (.547)
.010 (.015)
.010 (.018)
Change in LocalOwnerTV -.006 (.422)
-.006 (.006)
-.006 (.012)
Year 2007 Intercept
-.025 (.263)
-.032 (.150)
-.032 (.048)
Year 2009 Intercept
-.062 (.259)
.444 (.168) **
.444 (.070) **
Change in RadioNewsStations
Change in Co-ownedTV
.070 (.432)
.070 (.211)
.070 (.403)
Change in TV/Radio
.189 (.547)
.189 (.314)
.189 (.201)
Change in LocalOwnerTV -.343 (.422)
-.343 (.345)
-.343 (.166) *
Year 2007 Intercept
-.032 (.263)
-.032 (.150)
-.032 (.048)
Year 2009 Intercept
.444 (.259)
.444 (.168) **
.444 (.070) **
Change in LocalNewsHours
Change in Co-ownedTV
-.339 (.432)
-.339 (.929)
-.339 (.597)
Change in TV/Radio
-.284 (.547)
-.284 (.892)
Change in LocalOwnerTV -.478 (.422)
-.478 (.852)
-.478 (.137) **
Year 2007 Intercept
1.905 (.263) **
1.905 (.551) ** 1.905 (.143) **
Year 2009 Intercept
6.876 (.259) **
6.876 (.571) ** 6.876 (.074) **
Change in LocalNewsRating
Change in Co-ownedTV
.069 (.432)
.069 (.095)
.069 (.073)
Change in TV/Radio
.090 (.547)
.090 (.094)
.090 (.156)
Change in LocalOwnerTV -.007 (.422)
-.007 (.065)
-.007 (.020)
Year 2007 Intercept
-.306 (.263)
-.306 (.074) ** -.306 (.030) **
Year 2009 Intercept
-.340 (.259)
-.340 (.051) ** -.340 (.032) **
Change in LocalEveningRating
Change in Co-ownedTV
-.093 (.432)
-.093 (.061)
-.093 (.107)
Change in TV/Radio
.108 (.547)
.108 (.128)
.108 (.069)
Change in LocalOwnerTV -.050 (.422)
-.050 (.052)
-.050 (.103)
Year 2007 Intercept
-.308 (.263)
-.308 (.059) ** -.308 (.010) **
Year 2009 Intercept
-.607 (.259) **
-.607 (.051) ** -.607 (.042) **
Num. Obs. = 2050
* Significant at the 95% confidence level.
R-squared = .2808
** Significant at the 99% confidence level.
Adj. R-squared = .2719


Table 3. Estimation Results: All Ownership Variables, 2007-2009 Data Only





Clustered S.E.


Point Std.

Point Std.

Point Std.

Qual. Media Ownership

Est. Err.

Est. Err.

Est. Err.

Change in NewspaperCirculation
Change in MinorityOwnerTV -.031 (.96)
-.031 (.02)
-.031 (.02)
Change in TV/Newspaper
-.003 (1.70)
-.003 (.02)
-.003 (.03)
Change in Co-ownedTV
.005 (.79)
.005 (.01)
.005 (.01)
Change in TV/Radio
.039 (1.14)
.039 (.04)
.039 (.05)
Change in LocalOwnerTV
-.019 (.73)
-.019 (.01)
-.019 (.02)
Year 2009 Intercept
-.061 (.27)
-.061 (.01) **
-.061 (.01) **
Change in RadioNewsStations
Change in MinorityOwnerTV
.153 (.96)
.153 (.43)
.153 (.20)
Change in TV/Newspaper
1.435 (1.70)
1.435 (1.73)
1.435 (1.14)
Change in Co-ownedTV
-.442 (.79)
-.442 (.37)
-.442 (.12) **
Change in TV/Radio
.449 (1.14)
.449 (.37)
.449 (.19) **
Change in LocalOwnerTV
-.217 (.73)
-.217 (.34)
-.217 (.27)
Year 2009 Intercept
.489 (.27)
.489 (.17) **
.489 (.05) **
Change in LocalNewsHours
Change in MinorityOwnerTV 1.216 (.96)
1.216 (1.64)
1.216 (.25) **
Change in TV/Newspaper
.395 (1.70)
.395 (2.78)
.395 (1.15)
Change in Co-ownedTV
-1.694 (.79) *
-1.694 (1.45)
-1.694 (.27) **
Change in TV/Radio
-3.564 (1.14) ** -3.564 (1.76) *
-3.564 (.29) **
Change in LocalOwnerTV
-.252 (.73)
-.252 (1.56)
-.252 (.29)
Year 2009 Intercept
6.927 (.27) **
6.927 (.61) **
6.927 (.11) **
Change in LocalNewsRating
Change in MinorityOwnerTV -.109 (.96)
-.109 (.09)
-.109 (.09)
Change in TV/Newspaper
-.257 (1.70)
-.257 (.13)
-.257 (.18)
Change in Co-ownedTV
.158 (.79)
.158 (.13)
.158 (.24)
Change in TV/Radio
.337 (1.14)
.337 (.24)
.337 (.16) *
Change in LocalOwnerTV
-.008 (.73)
-.008 (.07)
-.008 (.10)
Year 2009 Intercept
-.346 (.27)
-.346 (.05) **
-.346 (.06) **
Change in LocalEveningRating
Change in MinorityOwnerTV
.042 (.96)
.042 (.09)
.042 (.14)
Change in TV/Newspaper
-.155 (1.70)
-.155 (.14)
-.155 (.13)
Change in Co-ownedTV
.025 (.79)
.025 (.08)
.025 (.05)
Change in TV/Radio
.026 (1.14)
.026 (.13)
.026 (.18)
Change in LocalOwnerTV
-.138 (.73)
-.138 (.06) **
-.138 (.04) **
Year 2009 Intercept
-.621 (.27) **
-.621 (.05) **
-.621 (.04) **
Num. Obs. = 1025
* Significant at the 95% confidence level.
R-squared = .4169
** Significant at the 99% confidence level.
Adj. R-squared = .3993


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