There have been no academic studies of what has happened to advertising prices after
2006. However, discussions with at least one industry expert indicate that the quantity of
commercials aired on most stations may have fallen quite significantly to around 8 min
per hour in 2015, from the 12 or more minutes per hour observed around 2000.
41
Understanding whether this change has been driven by the exercise of market power,
or changes in demand, as listeners or advertisers have become more able to substitute
to other media seems to present an interesting topic for future research that could also
help to improve our understanding of what happened in earlier years.
8.5.2 The Effects of Local Market Consolidation on Product Differentiation
and Variety
Another strand of the empirical literature has studied the relationship between local mar-
ket ownership concentration and either the aggregate variety of programming that is
available or measures of differentiation between different stations. These studies have
been motivated by the fact that, even though antitrust analysis of radio mergers has
focused on advertising price effects, changes in product variety or positioning may them-
selves have large effects on listener welfare as well as affecting how mergers change adver-
tising prices.
42
In addition, changes in station programming occur quite frequently,
although format changes can be costly (
Jeziorski, 2014b; Sweeting, 2013 provide esti-
mates of these costs). Another motivation comes from the perception that radio program-
ming has become more homogeneous across the country in the last two decades, and
there is interest in testing whether the popular presumption that this is due to the increas-
ing role of media conglomerates such as Clear Channel is correct.
43
Before discussing the empirical evidence in more detail, it is worth pointing out that
one can think of measuring differentiation or variety in radio programming in a number
of different ways. In a standard, two-firm Hotelling line model, it is usual to think of an
increase in the degree of differentiation between the firms as synonymous with an
increase in variety. But things become potentially more complicated when we enrich
the model so that it can more realistically be applied to radio markets. For example,
within many formats in large urban markets, there are three or more stations. How should
we measure variety when some sub-groups of stations are quite similar to each other?
Even more importantly, how should we account for the fact that a given station may play
41
Conversation with John Lund of Lund Media Research, February 20, 2015. As will be discussed in
Section 8.7, Clear Channel was one of the first firms to explicitly have a policy of reducing the number
of commercials, with its “Less is More” strategy in 2004.
42
Readers should consult section 6.4 of Chapter 6 in this volume for a discussion of the relevant theory for
antitrust analysis.
43
Future of Music Coalition (2003) provide evidence that formats have become increasingly homogeneous
and link this to consolidation.
Foege (2009) argues that Clear Channel, in particular, has been responsible
for a decline in the variety on radio.
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Handbook of Media Economics
several different programs and, in the case of music stations, hundreds of songs in the
course of a single day? For example, if two stations both use identical playlists with
500 songs, but rarely play the same songs at the same time, is there more or less variety
than if the stations use two playlists that do not overlap at all but only have 50 songs each?
In the empirical literature, a number of different metrics have been used, and some of the
differences in the results may reflect these choices.
Berry and Waldfogel (2001) examine how ownership concentration affects aggregate
variety using quasi-experimental variation created by the 1996 Telecommunications Act.
While the Act raised the limit on how many stations a single firm could own in all mar-
kets, the increases were greater in larger markets, or more specifically markets with more
stations. For example, in markets with more than 45 stations (true of the largest US cities
such as New York and Chicago), the limit increased from four to eight stations, while in
markets with 15–30 stations, the limit only increased from four to six stations.
44
Berry and
Waldfogel therefore try to infer the effect of increases in common station ownership by
examining how variety, measured by a count of the number of programming formats
available in a market, based on format definitions in Duncan’s American Radio publica-
tions, changed differentially across markets of different sizes between Spring 1993 and
Spring 1997, around the time of the Act. This identification strategy assumes, of course,
that other formatting trends affected markets of different sizes in the same way over this
time period.
45
Their results are consistent with ownership concentration increasing vari-
ety, although they also show that firms owning multiple stations tend to cluster stations in
similar, but not identical, formats (for example, Soft Adult Contemporary and Hot Adult
Contemporary). At the same time, in their working paper (
Berry and Waldfogel, 1999b),
they find no effect of ownership on total listenership, using the same identification
strategy.
In contrast,
Sweeting (2010) uses detailed station-level playlist and station-level
Arbitron audience data for a sample of contemporary music stations to examine how
common ownership affects product differentiation between particular stations and
station-level audiences. The focus is on stations in the same broadly defined format
category (part of BIA’s detailed format classification system), which collects together
44
Originally, the rules were defined using so-called contour rules that examined how many stations’ signal
coverage areas overlapped. However, these rules were fairly opaque to apply, which created a degree of
legal uncertainty for firms considering ownership transactions. In 2003, the FCC decided to use station
counts based on Arbitron market definitions. See FCC Report, Order and Notice of Proposed Rule Mak-
ing 03-127 (
http://hraunfoss.fcc.gov/edocs_public/attachmatch/FCC-03-127A1.pdf, accessed January
2, 2014). However, this change led to some strategic manipulations of which stations were included
in Arbitron local markets, so some additional rules were introduced (see discussion in http://www.
commlawblog.com/tags/arbitron-market-definition/, accessed January 2, 2014).
45
Berry and Waldfogel try to address this using earlier data and a difference-in-difference-in-difference spec-
ification. However, the possibility that there were some changes in formatting or music classification that
particularly affected the largest radio markets in the mid-1990s remains a potential concern.
365
Radio
stations in similar formats. For example, the “Adult Contemporary” format category
contains formats such as Adult Contemporary, Soft AC, Lite AC, Lite Rock, and Soft
Rock. The identification strategy involves looking at how the similarity of station play-
lists, measured in various ways, changes when pairs, or small groups, of stations become
commonly owned or cease to be commonly owned.
46
Consistent with the spirit of Berry
and Waldfogel’s results, in the sense that they view the driving force behind increased
variety as a desire to avoid audience cannibalization, Sweeting finds that common owners
tend to differentiate their stations. He also finds that the merging stations tend to signif-
icantly increase their combined audience.
However, Sweeting also shows that, at the same time, common owners make at least
some of their stations more similar to stations owned by other firms, and that the listen-
ership of these stations tends to fall, by about as much as the merging stations gain, so that
when one looks at format listening as a whole, ownership consolidation is not associated
with significant changes, consistent with
Berry and Waldfogel’s 1999b listenership result.
To capture the intuition for what seems to happen, suppose that there are three indepen-
dent stations, A, B, and C, arranged in a two-dimensional product space, and that initially
they are arranged symmetrically (say, at the vertices of an equilateral triangle). Following a
merger between the owners of stations A and B, suppose that the new common owner
differentiates them by moving B further away from A. Potentially it could do so by mak-
ing the station more differentiated from C as well, but the data suggests that it actually
makes at least one of its stations more similar to C than it was pre-merger, to try to take
listeners from C, as might happen in a spatial model where price competition is relatively
limited, and, as seems plausible for radio, there is limited scope to increase total radio or
format listenership by introducing completely new programming.
47
Sweeting’s results come with the caveat that there is no quasi-experimental variation
in ownership at the station level. Reassuringly, however, the patterns in the data are quite
similar looking at both changes in local market structure that result from very large
national transactions involving many hundreds of stations in different markets and differ-
ent formats, such as the 2000 AMFM–Clear Channel transaction, and local transactions
involving trades of stations in an individual market. For large transactions, the claim that
46
One approach defines different artists as different dimensions of the product space, and then uses a station’s
playlist to identify a location in this high-dimensional space. The difference between two playlists can be
measured by the angle between the location vectors at the origin. Alternative approaches include simply
looking at the proportion of playtime devoted to artists who are not played at all on other stations, and, for
small groups of stations, the total number of different artists played. In a working paper (
Sweeting, 2004),
Sweeting also projected the main artists in a format category into a two-dimensional space and then placed
the stations in this product space based on the artists appearing on their playlists. All of these measures
produce qualitatively similar results.
47
Studies by Borenstein (1986) and Rogers and Woodbury (1996), using data from prior to ownership
deregulation, support the contention that there is significant cannibalization both within programming
formats and at the aggregate level.
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Handbook of Media Economics
an omitted variable that was causing changes in differentiation to occur could also have
caused the change in ownership seems particularly unlikely.
Subsequent research using station-level data on programming content has found
results consistent with the claim that common owners differentiate their stations when
they are in the same or similar formats. For example,
Chipty (2007), using her cross-
section of data collected by the FCC, finds some evidence that this is true for both music
and non-music (e.g., news and sports) programming. On the other hand, recent results
using stations’ reported format labels, such as
Waldfogel (2011a), who examines how
ownership affected news programming in 2005 and 2007, have tended to find weaker
relationships. As well as reflecting the fact that format names are necessarily coarse mea-
sures of content (for example, a politically right-leaning and a politically left-leaning talk
station might be quite different, but both would usually be reported as being in the
“Talk” format), this pattern may also reflect the fact that the type of detailed format names
used in this type of study could often reflect differences in station marketing rather than
real differences in content. From a research perspective, the fact that format names may be
of limited use for some questions of interest is unfortunate because these labels are much
more accessible to researchers than airplay data.
48
As mentioned in the previous subsection, Jeziorski (2014a) quantifies the welfare
effects of changes in consolidation between 1996 and 2006. His structural model allows
him to identify effects that come through station owners being able to change their sta-
tion formats. The effects, and the implications for welfare, are quite complicated because
common owners are predicted to engage in some redistribution of commercials across
stations (see his table 15 for a full breakdown of the results). Jeziorski estimates that, hold-
ing advertising quantities fixed, the increase in format variety between 1996 and 2006
would have raised listener welfare by 0.3%. Changes in advertising quantities—where
owners of multiple stations tend to redistribute commercials toward their more popular
stations—tend to slightly reduce the gain to listeners (so their welfare only goes up by
0.2%), but advertiser surplus falls substantially by 21.4% as advertising prices rise and lis-
teners redistribute across stations to avoid commercials.
49
In contrast, advertiser welfare
would only fall by 5%, and listener welfare would be essentially unchanged, if advertising
quantities were allowed to change with formats held fixed. Unfortunately, because lis-
teners do not pay a price for listening to the radio, it is not possible to compare the effects
48
Of course, format labels are useful for asking questions about the provision of (say) news, classical music or
Spanish-language programming where a coarse classification is sufficient. One should, however, be aware
that format classifications can, in some ways, be both too coarse but also sometimes too fine. For example,
Soft Rock and Soft AC stations often have almost indistinguishable playlists.
49
One feature of Jeziorski’s model that leads to this result is that common owners can increase the price of
advertising by redistributing commercials from their least popular to most stations even if this increases the
total number of advertising exposures. This would not be possible if the advertising price per 1000 was
assumed to be declining in the number of exposures.
367
Radio
on listeners and advertisers in dollar terms. However, for both sides of the market, Jeziors-
ki’s results suggest that welfare effects that come through changes in product positioning
are greater than the changes that come through advertising prices when programming is
held fixed. This is an important conclusion, which is also consistent with the results of
Fan
(2013)
from local newspaper markets, as most merger analyses consider only price and
quantity changes, treating product varieties as given.
A separate but related strand of the literature has examined how well radio serves
minority audiences, and how this may have been influenced by ownership consolidation.
This is partly motivated by the concern in early theoretical work, such as
Steiner (1952),
that competing media outlets would provide insufficiently differentiated programming as
they competed for the ears or eyes of the majority. A different concern is that large,
publicly-traded corporations may be less willing or able to serve minority audiences than
local broadcasters and/or businesses owned by minorities, an issue which may be com-
pounded by the fact that minority listeners may be less valued by potential advertisers. In
the 1970s, the government started the Minority Telecommunications Development Pro-
gram to facilitate minority ownership of broadcast stations,
50
and as part of its 2004 deci-
sion requiring the FCC to re-examine a number of relaxations to ownership rules for
radio and television stations, the Court of Appeals for the Third Circuit reaffirmed
the validity of promoting minority ownership as a goal for media policy.
51
One reason why this question is interesting is that the black and Hispanic populations
tend to have different programming tastes to the rest of the population. For example,
around 50% of black (Hispanic) listening is to stations in Urban (Spanish-language) for-
mats, whereas Urban stations account for less than 5% of non-black listening and Spanish-
language stations, unsurprisingly, attract almost no non-Hispanic listeners.
52
As part of his
research on preference externalities (see also
Chapter 1 in this volume), Waldfogel (2003)
shows that increases in the number of blacks or Hispanics in a market has a large and sta-
tistically significant effect on the number of Urban or Spanish-language stations so that it
is minority populations in markets where they really are minorities that are most likely to
be underserved. One issue that has affected minority-oriented stations in recent years has
been the introduction of Arbitron’s PPM measurement technology. PPM estimates of
Spanish-language and Urban station listenership were significantly lower than estimates
based on more traditional diaries, and industry experts have suggested that these lower
estimates, by making advertisers less willing to pay for commercial time, may have led
to a significant reduction in the number of minority stations in recent years.
53
50
http://en.wikipedia.org/wiki/Minority_ownership_of_media_outlets_in_the_United_States (accessed
February 25, 2014).
51
373F.3d 372, p. 35.
52
Religious programming also attracts large minority audiences. See Arbitron Company (2012b,c) for
details on format listenership for minority groups.
53
Conversation with John Lund of Lund Media Research, February 20, 2015. See Napoli (2010) for an
extended discussion of controversies regarding PPM.
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Handbook of Media Economics
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