Market structure and media diversity.
Hiller, R. Scott ; Savage, Scott J. ; Waldman, Donald M. 等
I. INTRODUCTION
Information on news and current affairs can raise political
awareness and promote a range of ideas. Under the assumption that
unregulated media markets supply too little variety, many societies have
charged regulators with ensuring there are sufficient opportunities for
different, new, and independent viewpoints (diversity), and that media
respond to the interests of their local communities (localism). (1) The
Federal Communications Commission (FCC) has traditionally limited the
amount of common- and cross-ownership of newspapers, radio, and
television (TV) stations. Recently, the FCC relaxed ownership rules and
refocused their attention on market forces, for example, consumer
preferences and new media, such as satellite, the Internet, and
Smartphone, in order to achieve their diversity and localism goals.
Given the increase in choices through new media, supporters of greater
ownership concentration argue that traditional media should be free to
consolidate and use the efficiencies to provide more diverse and local
news programming. Opponents question whether such efficiencies are
achievable, and argue that large, consolidated media corporations are
not flexible enough to serve the interests of local and minority
communities.
In this paper, we estimate consumer preferences for their local
news and current affairs (news service) described by the offerings from
newspapers, radio, TV, the Internet, and Smartphone. News service
characteristics are: diversity of opinion in the reporting of
information, coverage of multiculturalism issues, amount of information
on community news and events, and amount of space or time devoted to
advertising. We use our demand estimates to calculate the impact on
consumer welfare from a change in media market structure that reduces
the number of independent TV stations in the market by one.
Specifically, we employ the willingness-to-pay (WTP) construct to
measure the expected consumer welfare effects between the news service
supplied to the consumer before the change in market structure and the
service supplied after the change. We focus on broadcast TV stations
because they are the main news for most households and because the FCC
has direct oversight of their ownership.
We estimate our demand model with data obtained from a nationwide
survey of households during March, 2011. Results show that the
representative consumer is willing to pay from $18 to $26 per month for
an increase in diversity of opinion (and slightly more for community
news) from a low to a medium level, but only an additional $6 to $7 to
move to a high level of diversity of opinion (or community news). The
representative consumer also values an improvement in information that
reflects the interests of women and minorities from low to medium ($7).
The representative consumer is willing to pay about $5 to avoid a change
from a low to a medium level of advertising, but the much higher amount
of $20 to avoid a change from a medium to a high level. Using FCC (2011)
data on media market structure, we present evidence that indicates the
amount of diversity, localism, and advertising in the news services
supplied to consumers is lower in markets with one fewer independent TV
station. As a result, the average "small market" (i.e., five
or fewer TV stations) consumer loses $.83 per month, whereas the average
"large market" (i.e., 20 or more TV stations) consumer loses
$.37 per month. These losses are equivalent to $45 million annually for
all small-market households in the United States and $13 million for
large-market households. If the change in market structure occurs in all
markets, aggregate losses nationwide would be about $681 million.
Other studies have measured the relationship between information on
news and current affairs and market structure. Flowever, these studies
measure supply from just one of the media sources that comprise the
consumer's news service; for example, Milyo (2007), Gentzkow
(2007), and Gentzkow and Shapiro (2010) for newspapers, and Siegelman
and Waldfogel (2001) and Crawford (2007) for radio and TV. Our research
is also related to studies that quantify the relationship between
quality and market structure for different industries. For example,
Mazzeo (2003) shows that average flight delays are longer in more
concentrated airline markets. Goolsbee and Petrin (2004) estimate that
cable TV channel capacity, number of over-the-air channels, and number
of premium movie channels increased in response to satellite entry.
Matsa (2011) finds that supermarkets facing more intense competition
have more products available on their shelves, while Olivares and Cachon
(2009) show that the inventories of General Motors dealerships increases
with the number of competitors. In contrast, Domberger and Sherr (1989)
find no correlation between the threat of new entry and customer's
satisfaction with their attorney used for home purchases. Because we
measure the change in market structure by reducing the number of
independent TV stations, our paper is also related to structural models
of differentiated oligopoly that predict the price effects from a
simulated merger; for example, Nevo (2000) for breakfast cereals, Pinske
and Slade (2004) for U.K. brewing, and Ivaldi and Verboven (2005) for
car manufacturing.
Relative to these literatures our study makes several
contributions. First, we offer new evidence from media markets by
examining the consumer welfare effects from a news service bundled from
newspapers, radio, TV, the Internet, and Smartphone. Second, the
prediction of non-price effects appears to be novel in the simulated
merger literature. Finally, by looking at a vector of non-price effects
we are able to document a new and interesting tradeoff between the
diversity and localism characteristics of news service, and advertising.
That is, the amount of diversity and localism declines following a
decrease in the number of independent TV stations, which is a cost to
the typical consumer, but so too does the amount of advertising, which
is a benefit to the typical consumer. This finding should be interesting
to antitrust officials and policy makers because it provides a new angle
from which to assess the benefits and costs of media mergers and
ownership rules.
The article is organized as follows. Section II outlines the demand
model. Data are described in Section III. Section IV presents demand
estimates and calculates consumer valuations for the diversity,
localism, and advertising characteristics of a news service. Section V
uses the valuations from Section IV to conduct a simple policy
experiment that estimates the impact on consumer welfare from a change
in market structure. Section VI concludes.
II. DEMAND MODEL
A. Household Choice for News Services
There are several problems when estimating demand for news service
with market data. First, households consume a bundle of entertainment
and news services from the offerings from newspapers, radio, TV, the
Internet, and Smartphone, but data on these bundles, their non-price
characteristics, and prices are not readily available. Second, even when
available, these data are unlikely to exhibit sufficient variation for
the precise estimation of demand parameters. For example, the levels for
the diversity and localism characteristics are often highly, positively
correlated. Third, news services are a mixture of private and public
goods and many households, for example, those who bundle broadcast radio
and TV stations, make no direct payment for consumption. Because
detailed data on the amount of advertising within household bundles are
not available, it is not possible to accurately measure the full cost of
news services.
We overcome these problems by using an indirect valuation method.
This approach is widely used in the environmental economics and
transportation choice literature that employs market and experimental
data. The market data are the news service households currently consume.
The experimental data are a set of constructed news services. We design
a choice set that manipulates the levels of the characteristics of the
constructed news services to obtain the optimal variation in the data
needed to estimate the demand parameters precisely. Respondents first
choose between a pair of constructed news service alternatives (A and
B), and then between that choice (say, for example, A) and their actual
news service at home (status quo). (2) Because our design exogenously
determines the levels of the characteristics of each news service, and
randomly assigns the levels across respondents, we limit measurement and
collinearity problems. Furthermore, by asking respondents to complete
eight such choice occasions, we increase parameter estimation precision,
and reduce sampling costs by obtaining more information on preferences
for each respondent.
The conditional indirect utility function for household n from news
service alternative j on choice occasion t is assumed to be:
(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [alpha] is the marginal utility of price, p is price,
[[beta].sub.n] is a vector of consumer-specific marginal utility
coefficients, [x.sub.njt], is a vector of observed non-price
characteristics of entertainment and news service, [[xi].sub.njt] is the
utility from unobserved entertainment services and from other dimensions
of news not included in [x.sub.njt], and [[epsilon].sub.njt] is an
unobserved random error that is independently and identically
distributed extreme value. The effect on utility from price is specified
to vary by three income groups, with the dummy variable [y.sub.gn]
indicating income group g. The marginal utility of price for the low
income group (i.e., household income of $25,000 or less) is [alpha] and
the marginal utility of price for a household in income group g is
[alpha] + [[alpha].sub.g]. The income dummy variables are [y.sub.2n] (or
MED_INCOME), which equals one when household income is greater than
$25,000 and less than $50,000 and zero otherwise, and [y.sub.3n] (or
HIGH_INCOME), which equals one when household income is greater than
$50,000 and zero otherwise.
The density of the distribution for [[beta].sub.n] is
f([[beta].sub.n]|[theta]) with the vector [theta] containing the mean
and covariance parameters of [[beta].sub.n]. Assuming [[beta].sub.n] = b
+ [[eta].sub.n], utility can be rewritten as:
(2) [U.sup.*.sub.njt] = [alpha][p.sub.njt] + [summation over
(g=2,3)][[alpha].sub.g][p.sub.njt][y.sub.gn] + b'[x.sub.njt] +
[[eta].sub.n][x.sub.njt] + [[xi].sub.njt] + [[epsilon].sub.njt]
where b is the population mean marginal utility from a vector of
non-price characteristics and r|., is the individual consumer's
deviation from this mean. Given e is distributed extreme value, and
assuming an appropriate distribution for [[beta].sub.n], mixed logit
estimation of Equation (2) is possible by simulated maximum likelihood
(Brownstone and Train 1999; Revelt and Train 1998). In our choice
scenario in Section III, the consumer chooses between three alternatives
in each choice occasion that differ in their levels of [x.sub.njt] and
[p.sub.njt] only. By holding all other dimensions of entertainment and
news services in Equation (2) constant so that [[xi].sub.njt] =
[[xi].sub.n], the model controls for potential correlation between price
and quality that is not observed by the researcher. (3)
As indicated in Table 1, the levels of the characteristics that
comprise the elements of the vector [x.sub.njt] and [p.sub.njt].
DIVERSITY OF OPINION ([x.sub.1]) is the extent to which the information
on news and current affairs in the household's news service
reflects different viewpoints. MULTICULTURALISM ([x.sub.2]) is the
amount of information on news and current affairs that reflects the
interests of women and minorities. COMMUNITY NEWS ([x.sub.3]) is the
amount of information on community news and events, and ADVERTISING
([x.sub.4]) is the amount of space and/or time devoted to advertising.
COST (p) is the dollar amount the household pays per month for their
news service. That is, the total of monthly subscriptions to all media
sources, plus any contributions to public radio or public TV stations.
B. WTP for News Service Characteristics
It is convenient to convert the estimated marginal utilities for
changes in [x.sub.njt] into WTP. For example, the mean WTP for a one
unit increase in diversity of opinion ([WTP.sub.d]) for each income
group can be calculated from our estimates of utility as:
(3) [WTP.sub.d] = -[b.sub.1]/[[alpha] + [summation over
(g=2,3)][[alpha].sub.g][y.sub.gn]]
where [b.sub.1] is the mean marginal utility of DIVERSITY OF
OPINION. (4) This approach to estimating consumer valuations is used for
the three other non-price characteristics.
Because WTP in Equation (3) is a function of random variables, it
is also a random variable with appropriate standard errors. We calculate
standard errors for WTP using a bootstrapping method. First, marginal
utility values for individual household n are drawn from the
multivariate normal distribution implied by the mean (b) and covariance
([[eta].sub.n]) parameters estimated from Equation (2). The marginal
utilities are then substituted into Equation (3) to calculate household
n's WTP and the mean WTP for all households. This simulation is
repeated 1,000 times to derive the sampling distribution for WTP for
each income group g. Standard errors for WTP, by income group g, are
estimated from this sampling distribution.
III. DATA
A. Survey Design
The WTP for local media environment characteristics are estimated
with data from an online survey questionnaire. The questionnaire begins
with a cognitive buildup section that describes the respondent's
local news service in terms of the offerings from newspapers, radio, TV,
the Internet, and Smartphone. Respondents are asked questions about
their media sources, how much information they consume from each source,
the cost of their media sources, and the levels of the four different
characteristics of their news service described in Table 1.
Cognitive buildup is followed by the choice scenarios. Information
from the cognitive buildup questions is used to summarize each
respondent's actual entertainment and news service at home with
respect to their media sources, the levels of the non-price
characteristics of their service, DIVERSITY OF OPINION,
MULTICULTURALISM, COMMUNITY NEWS, and ADVERTISING, and their COST. A
table summarizing the sources and characteristics of the
respondent's actual media environment at home is presented to the
respondent before the choice scenario. The respondent is then instructed
to choose in eight choice occasions. In each occasion, the choice is
between their actual news service at home and two constructed new
service alternatives, labeled A and B, that differ by the levels of
their non-price characteristics of service, and cost.
We used market data from newspapers, radio and TV stations,
Internet and cellular telephone providers, a pilot study, and three
focus groups to test and refine our descriptions of the news service
alternatives. Measures developed by Huber and Zwerina (1996) were used
to generate an efficient nonlinear optimal design for the levels of the
constructed news characteristics. A fractional factorial design created
72 paired descriptions of A and B news services that were grouped into
nine sets of eight choice questions. The nine choice sets were
rebalanced to ensure that each household faced a range of costs that
realistically portrayed the prices for media sources in their local
market. For example, a respondent who indicated that they pay nothing
for their news source was exposed to a range of costs that included zero
dollars per month. The nine choice sets, along with the order of the
eight A-B pair choice alternatives within each choice set, were randomly
assigned to respondents.
B. Survey Administration
During the week of March 7, 2011, Knowledge Networks Inc. (KN)
randomly contacted a gross sample of 8,621 panel members to inform them
about the survey. The survey was fielded from March 11 to March 21. A
total of 5,548 respondents from all 50 states and the District of
Columbia completed survey questionnaires. Because of incomplete survey
responses, we trimmed the sample by 417 respondents. The median
completion time for our sample of 5,131 respondents with complete
information was about 17 minutes.
We compared selected demographics for U.S. population, for all
KN's panel members, and for panel members who were invited to
participate in this survey (Knowledge Networks Inc. 2010; United States
Census Bureau 2009). The demographics for all KN panel members are
similar to those reported by the Census Bureau. Apart from race and
employment status, the demographics for the gross sample of panel
members invited to participate in this study and the final sample of
respondents who completed questionnaires are also similar to those
reported by the Census Bureau. However, estimates from a probit model
that compared respondent's characteristics between the gross and
final samples also indicate potential differences in age, gender,
education, and Internet access between our final sample and the
population. We remedied this possible source of bias in our demand
results by estimating with weighted maximum likelihood, where the
contribution to the log-likelihood is the post-stratification weight
times the log of the choice probability for the choice occasion. (5)
C. Media Sources and News Service
Summary statistics for news service characteristics are presented
in Table 2. These data indicate that, on average, the levels of the
DIVERSITY OF OPINION, MULTICULTURALISM, COMMUNITY NEWS, and ADVERTISING
characteristics were about "medium." About 58% of respondents
indicated that they bundled their subscription TV service with their
Internet and/or telephone service. The price (p or, COST) for the
typical media combination ranged from zero to $447 per month, with an
average of $ 111.20. About 10% of the sample indicated that they have
contributed $117, on average, to public radio and/or TV stations during
the past 12 months. This is reasonably close to the combined annual
costs of membership at 2011. For example, Rocky Mountain PBS offers an
annual membership for $40 and Colorado Public Radio for $120. These
membership costs vary between states.
This article presents a first attempt at measuring preferences for
news service characteristics from a bundle of media sources, and
self-reported data is the only avenue to examine these non-price
characteristics. Because they are self-reported, there may be some
concern about the accuracy of the data describing the characteristics in
our sample. We address these concerns by testing the relationships
between our measures of diversity and localism and alternative measures
from the FCC (2011) and Gentzkow and Shapiro (2010). The results,
available in Table A2 of Appendix SI, show that the information reported
by survey respondents is a reasonably good proxy for the diversity of
news service alternatives in U.S. markets. (6) For example, there is a
positive correlation between the number of TV stations broadcasting
multiple channels and DIVERSITY OF OPINION, positive correlations
between the number of non-commercial radio and TV stations and DIVERSITY
OF OPINION, and a positive correlation between the number of different
radio formats and DIVERSITY OF OPINION. The results also show that high
newspaper slant, as measured by Gentzkow and Shapiro, is correlated with
lower DIVERSITY OF OPINION.
D. Market Structure
We use FCC (2011) data to measure media market structure. The
important variables of interest are the number of full-power independent
TV stations in the market (VOICES) and the number of full-power
independent and non-independent TV stations in the market (STATIONS).
VOICES is measured by first combining all the TV outlets within each
market. The listing of the unique parent company identifiers of all
attributable owners of an outlet (voiceprint) is then created, sorted
alphabetically, and duplicate voiceprints are eliminated. The parent
identifier is then used to count the number of voices in the voiceprint
for each outlet. Voiceprints composed of a single voice are added to the
voice count of the market, while any voiceprint that includes one of the
voices counted at the previous stage of the calculation is eliminated.
These are non-independent voices because their voice has been heard on
another outlet. This process is repeated based on the number of voices
in the voiceprint. The other market structure variables considered in
this analysis are available in Table 3.
Summary statistics are available in Table 4. Our sample covers 203
of the nation's 210 television markets. (7) As of December, 2009,
the total number of newspaper, radio, and TV outlets ranged from 4 to
291, with an average of 139 per market. On average, about 81% of media
outlets are radio stations, which partially reflects the geographical
definition of a TV market which can include several radio markets. When
examining the market structure data at the 75th percentile, we observe
that most markets are served by about 182 or fewer media outlets. A
similar pattern emerges in the bottom panel in Table 4 for small TV
markets with five or fewer stations. At December, 2009, the total number
of newspaper, radio, and TV outlets in small markets ranged from 4 to
86, with an average of 47 per market. On average, about 82% of media
outlets in small markets are radio stations, and as indicated by the
75th percentile, most small markets are served by about 57 or fewer
media outlets.
The number of TV stations (STATIONS) ranges from 1 to 27 across the
United States, with most markets having five or more TV stations. As
expected, small markets have limited variety and are typically served by
one station from the "big four" national networks, ABC, CBS,
FOX, and NBC, plus one public broadcasting and/or educational station
such as PBS. For example, Rochester, NY is comprised of 392,150 TV
households and is served by ABS, CBS, FOX, NBC, and PBS. In contrast,
New York is comprised of 7,493,530 TV households and is served by 23
stations. These include multiple channels from the big four networks,
several public broadcasting and educational channels (e.g., PBS, Public
TV), several non-English language channels (e.g., TeleFuturo,
TeleMundo), several other independent stations (e.g., Mountain
Broadcasting, Retro TV), and a religious channel (Trinity).
IV. DEMAND ESTIMATES
Weighted maximum likelihood estimates of the household utility
model are reported in Table 5. For purpose of comparison, in model (1)
we begin by reporting estimates from a standard conditional logit model
with fixed marginal utility parameters. Model (2) displays estimates
from a mixed logit model specification where the four non-price marginal
utility parameters are assumed to be independently normally distributed.
(8) Preferences may be correlated, for example, consumers who like more
diversity of opinion may also like more information on women and
minorities. Accordingly, the mixed logit model (3) permits correlation
between the non-price parameters. Model (4) reports estimates from a
mixed logit model specification with correlated non-price parameters
plus the two interactions, COST x MED_INCOME and COST x HIGH_INCOME. All
specifications include an alternative-specific constant to capture
differences in tastes between the consumer's actual news service at
home and the constructed A and B news services. All other things held
constant, this can be interpreted as the consumer's disutility from
switching from their actual news service at home to service A or B.
The data fit all specifications reasonably well as judged by the
sign and statistical significance of most parameter estimates. We focus
our discussion on the results from model (4) because that model is the
most general, permitting both correlation among the random parameters
and the marginal disutility of cost to vary by income, as specified in
Equation (2). The mean of each of the random marginal utility parameters
for DIVERSITY OF OPINION, MULTICULTURALISM, and COMMUNITY NEWS is
positive and significant at the 1% level, while the mean of the random
parameter for ADVERTISING is negative and significant. These estimates
imply that the representative consumer's utility increases when
there is more diversity in the reporting of news, more information on
women and minorities, more information on community news, and less space
and/or time devoted to advertising. The fixed parameter for COST is
negative and the parameters for COST x MED_INCOME and COST x HIGH_INCOME
are positive. These estimates imply that consumer's utility
decreases when the dollar amount paid for their news service increases
but that the effect diminishes with increases in household income. The
standard deviations of each of the random marginal utility parameters
are significant at the 1% level, indicating that tastes vary in the
population.
In the discussion above the coding of the four non-price features
in the household utility function is linear, which implies that the
marginal utilities are the same when moving from low to medium and from
medium to high levels. We relax this restriction by replacing each of
the four non-price characteristics with a pair of dichotomous variables.
Mixed logit estimates of the utility model with nonlinear preferences
and with correlated non-price parameters plus COST x MED_INCOME and COST
x HIGH_INCOME are presented in model (5) of Table 5. Focusing on the
means of each of the random marginal utility parameters, the results
indicate declining marginal utility for the representative consumer with
respect to diversity of opinion, multiculturalism, and community news.
Mean WTP estimates by household income for both the linear and the
nonlinear models are presented in Table 6. (9) In column 3, we observe
that the representative medium-income consumer is willing to pay $20.89
per month for an improvement in diversity of opinion from low to medium,
but only another $6.78 per month for an additional improvement to high
diversity of opinion. Similarly, the representative medium-income
household is willing to pay $24.92 per month for an initial improvement
in information on community news and events from low to medium, but only
another $6.17 per month for an additional improvement to high. The
marginal utility estimates for multiculturalism indicate that households
value an improvement in information that reflects the interests of women
and minorities from low to medium (i.e., WTP = $7.05) more than an
improvement from low to high (i.e., WTP = $4.06). In other words, the
representative medium-income household wants more, but not a lot more
information reflecting the interests of minorities. The marginal utility
estimates for advertising indicate a similar pattern to diversity of
opinion and community news, albeit in reverse. The representative
household is willing to pay about $15.94 per month (i.e. $20.66-$4.72)
for a move from high to medium advertising, but only an additional $4.70
per month to move from medium to low advertising. (10)
V. POLICY EXPERIMENT
The demand estimates provide information on the expected consumer
benefits from increased media diversity and localism. The question of
interest now is how do these benefits change with regulations that shape
media market structure? We shed light on this question by estimating the
relationships between the number of TV stations in the market and the
amount of diversity, localism, and advertising supplied within each
household's news service. The resulting supply response parameters
are then combined with WTP calculations to conduct a simple policy
experiment that measures the impact on consumer welfare from a change in
media market structure that reduces the number of independent TV voices
by one.
A. The Supply of News Services
Previous studies of media markets typically use industry databases
from BIA Financial Networks, Neilson Media Research, and ProQuest
Newsstand to measure the quantity and quality of news provided by
newspapers, radio, and TV stations (Crawford 2007; Gentzkow and Shapiro
2010; Greenstein and Zhu 2012; Groseclose and Milyo 2005; Yan and Napoli
2006). Because we are investigating a household's news service
bundled from all of their media sources, similar measures are not
desirable for this study. Instead, we use information on consumer's
news service at home to measure the characteristics supplied by news
service alternatives in different TV markets.
Consider a reduction in the number of independent TV voices in a
market as it impacts the single news service characteristic diversity of
opinion (d). A simple representation of the diversity of opinion
produced by alternative j for consumer n in television market m is:
(4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [d.sup.*.sub.njm] is the unobserved continuous index of the
diversity of opinion in respondent n's media, [VOICES.sub.m] is the
number of independent TV voices in the market, [STATIONS.sub.m] is the
number of TV stations in the market, [D.sub.n] is a vector of
consumer-specific demographic controls, [Z.sub.j] is a vector of news
service controls, the [delta]'s, [tau] and [gamma] are parameters,
and v is an independently and identically normally distributed error
term with zero mean and constant variance [[sigma].sub.v.sup.2]. The
interaction term is included to measure the different impacts from a
change in market structure in small- versus large-TV markets. The
[D.sub.n] vector measures the head of the household's age (AGE = 1
if 18-24 years; 2 if 25-34; 3 if 35-44; 4 if 45-54; 5 if 55-64; 6 if
65-74; 7 if 75 years or over), education (EDUC = 1 if less than high
school; 2 if high school; 3 if some college; 4 if bachelor's degree
or more), gender (GENDER = 1 if female; 0 otherwise), household income
(INCOME = 1 if less than $10,000; 2 if $10,000-$24,999; 3 if
25,000-$49,999; 4 if $50,000-$74,999; 5 if $75,000 or more), and race
(RACE = 1 if white; 0 otherwise). (11) The [Z.sub.j] vector includes
dummy variables to control for the 16 different media source
combinations in our sample that are comprised from newspapers, radio,
TV, the Internet, and Smartphone.
The respondent reports one of three possible levels for the
diversity of opinion feature, low, medium, or high, based upon her or
his level of [d.sup.*.sub.njm]:
(5)[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
where [mu] is the normalized unknown level of [d.sup.*.suyb.njm]
above which respondents report a high level for diversity of opinion.
Given v is normally distributed, Equations (4) and (5) represent the
conventional ordered probit model, which can be estimated by maximum
likelihood (McKelvey and Zavoina 1975). We estimate Equation (4) to
obtain the relationships between the number of TV stations in the market
and the four non-price characteristics of news service, and use these
estimates to approximate the supply-side responses from media outlets.
For ease of notation, we let X = VOICES and drop all subscripts
that indicate consumers, alternatives, and markets. The representative
consumer's expected benefit from the diversity of opinion in their
local news service is:
(6) E[[B.sub.d](X)] = [P.sub.dL](X)[v.sup.*.sub.dL] +
[P.sub.dM](X)[v.sup.*.sub.dM] + [P.sub.H](X)[v.sup.*.sub.dH]
where [P.sub.dL](X) is the probability that the consumer will be in
the low diversity of opinion state, [P.sub.dM](X) is the probability
that the consumer will be in the medium state, [P.sub.dH](X) is the
probability that the consumer will be in the high state, and
[v.sub.dL.sup.*], [v.sub.dM.sup.*], and [v.sub.dH.sup.*] are consumer
valuations for low, medium, and high diversity of opinion. We do not
observe [v.sub.dL.sup.*], [v.sub.dM.sup.*], and [v.sub.dH.sup.*].
However, we are able to calculate from Equation (2) the consumer's
WTP for a change from low to medium diversity of opinion
([DELTA][v.sub.dM]), and the WTP for a change from low to high diversity
([DELTA][v.sub.dH]). Writing [v.sub.dM.sup.*] = [v.sub.dL] +
[DELTA][v.sub.dM] and [v.sub.dH.sup.*] = [v.sub.dL.sup.*] +
[DELTA][v.sub.dH] and substituting this expression into the
consumer's expected benefit Equation (6) gives E[[B.sub.d](X)] =
[P.sub.dL](X)[v.sub.dL.sup.*] + [P.sub.dM](X)([v.sub.dL.sup.*] +
[delta][v.sub.dM]) + [P.sub.dH](X)([v.sub.dL.sup.*] +
[delta][v.sub.dH]). The effect of a change in X on the expected benefit
from diversity of opinion is:
(7) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [DELTA][P.sub.dM]/[DELTA]X and [DELTA][P.sub.dH]/[DELTA]X
measure the effects of a change in X on the predicted probability of
being in the medium and the high diversity of opinion states, and
[DELTA][P.sub.dL]/[DELTA]X + [DELTA][P.sub.dM]/[DELTA]X +
[DELTA][P.sub.dH]/[DELTA]X = 0, which follows from the requirement ill
at the three probabilities sum to one.
Estimates of [DELTA][P.sub.dM] and [DELTA][v.sub.dH] for the
typical consumer are obtained from Equation (2). We then use our
estimated coefficients from the ordered probit model of Equation (4) and
the sample data to calculate the predicted probability distributions for
low, medium, and high diversity of opinion in the "before"
environment. Holding all else constant, we then reduce the number of
independent TV voices by one in the sample data to approximate the
change in market structure, and re-calculate the predicted probability
distributions for low, medium, and high diversity of opinion in the
"after" environment. The difference in before-and-after
probabilities are used to form the change in probabilities,
[DELTA][P.sub.dM]/[DELTA]X and [DELTA][P.sub.dH]/[DELTA]X. These
calculations are repeated for the multiculturalism, community news, and
advertising characteristics, and then aggregated to reflect the general
population.
B. Relationship between Market Structure and News Services
Characteristics
Because unobserved cost and demand factors affect market structure
and the supply of news service characteristics, the estimated
coefficient on VOICES in Equation (4) is likely to suffer from omitted
variable bias. We account for this endogeneity with a two-step model
similar to Mazzeo (2002), Manuszak and Moul (2008), and Singh and Zhu
(2008). For the first step, we specify the representative independent TV
station's latent profits ([[pi].sup.*]) in market m = 1, 2, ..., M
to be a function of market size, variable profits per TV household, and
fixed costs. All variables are measured with market-level data from the
FCC (2011) and the National Climatic Data Center (2011) and are
described in Table A3 of Appendix SI. The market size variables are the
number of TV households (TV_HOUSES) and the projected annual average
population growth (POPjGROWTH). The variables that comprise variable
profit are median household income (MEDIUM_INCOME), the number of years
of education for the population over 25 years of age (EDUC_YEARS),
median age of the population (MEDIUM_AGE), percentage of the population
that is female (FEMALE_SHARE), percentage of the population that is
white (WHITEJIHARE), and population per square mile (DENSITY). Fixed
costs are approximated by measures of severe climate that increase the
expense of building and maintaining a TV broadcast antenna. They are
annual average snowfall (SNOW), annual number of days with the
temperature below freezing (FREEZE), and SNOW x FREEZE.
Ordered probit estimates of the first-step latent profits are
presented in Table A4 of Appendix S1. The estimated coefficients on
TV_HOUSES and POP_GROWTH indicate that profits are higher in larger
markets and that these markets can support more independent TV stations.
Household income and the share of female population also have a positive
impact on profits, while profits are lower in more densely populated
markets. The latter result may be due to the higher cost of marketing
and/or the rental price of land in urban and inner-city locations.
Profits are higher in markets with more snowfall although the estimated
parameter on SNOW is marginally insignificant. This suggests that the
fixed cost effect may be offset by the demand effect. Specifically,
because their households spend more time indoors with stronger desire
for TV, markets with more snow earn more revenue and can support more
independent TV stations. The estimated coefficients on FREEZE and SNOW X
FREEZE are both negative. Snow, sleet, and freezing rain can lead to ice
buildup and the eventual collapse of the broadcast antennae. Because the
fixed costs of constructing and maintaining more durable antennae are
higher in markets with of more snow and freezing weather, expected
profits are lower.
The estimated cutoff parameters and linear prediction from the
first-step ordered probit model of profits are used to construct the
modified correction term similar to the inverse Mills ratio in
Heckman's (1979) selection model:
(8) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [[??].sub.k] are the estimated k = 1, 2, ..., K cutoffs, K is
the maximum number of independent TV stations in the sample,
[[??].sub.m] is the linear prediction of latent profits, [phi] is the
standard normal density function and [PHI] is the standard normal
cumulative distribution function. The second-step estimates of Equation
(4) with the modified correction term [[??].sub.m] included as an
additional variable are available in Table 7. The estimated coefficients
on the correction term are statistically significant for the two
diversity characteristics, DIVERSITY OF OPINION and MULTICULTURALISM,
and marginally insignificant for the localism characteristic, COMMUNITY
NEWS. (12)
Because [[lambda].sub.m] is estimated in the first step, the
asymptotic variance of the second-step estimator is not valid. We report
bootstrapped standard errors for supply responses with 100 replications.
We also report the original supply coefficients, which are recovered
from the two-step model using the method described by Imbens and
Wooldridge (2007).
Focusing on the important variable of interest, we observe that
estimated coefficients on VOICES are positive for all non-price news
characteristics, while the estimated coefficients on VOICES X STATIONS
are negative. These results are consistent with Mazzeo (2003), Goolsbee
and Petrin (2004), Matsa (2011), and Olivares and Cachon (2009), and
suggest that in markets with one fewer independent TV station, consumers
are more likely to have less diversity of opinion, multiculturalism, and
community news in their news service. For example, the sample means of
the predicted probabilities of supply presented in the bottom panel of
Table 7, show that following the change in market structure, the
percentage of households in a low diversity of opinion state will
increase by 1.6, the percentage of households in a medium state will
increase by three, and the percentage of households in a high state will
decrease by 1.9. The results with respect to diversity of opinion,
multiculturalism, and community news are reasonably intuitive.
Consolidation of TV stations is associated with the softening of media
competition and the provision of less diversity and less local news,
which is costly to produce. (13,14)
As indicated in Table 7, markets with one fewer independent TV
station are more likely to have less advertising. This is consistent
with similar empirical findings for radio and TV markets which find that
broadcasters in concentrated markets scale back the amount of
advertising but charge higher advertising rates. For example, Crawford
(2007) finds that independent TV stations provide more advertising per
program but charge lower prices to advertisers. Brown and Alexander
(2004) find a positive correlation between TV market concentration and
the price of advertising per viewer. Following Cunningham and Alexander
(2004), they argue that when consumer's elasticity of TV viewing
with respect to advertising is weak, a decrease in the fraction of
broadcast time devoted to advertising can lead to a decrease in the
overall amount of advertising supplied and an increase in advertising
rates. Drushel (1998) finds that following the Act, increased radio
station concentration was positively correlated with higher advertising
rates.
C. Market Structure and Consumer Welfare
We use marginal WTPs for each individual respondent, calculated
from Equation (2), the predicted supply probabilities from Table 7, and
the expected benefit Equation (7), to measure the impact on consumer
welfare from the change in media market structure. The algorithm for
calculating the changes to consumer welfare is detailed in the online
appendix. As presented in Table 8 and Figure 1, estimates of the impact
on consumer welfare from a marginal decrease in the number of
independent TV stations for all market sizes ranging from 5 to 20 TV
stations. Columns 3 through 6 of Table 8 report average consumer welfare
per month and columns 7 through 12 report annual aggregate welfare. (15)
The first interesting observation is that the average welfare effects
per month depend on market size, with smaller markets having larger
effects in absolute terms. The intuition for this finding is clear. The
impact from the loss of an independent voice in the market will be more
acute when there are fewer competitors to fill the void. As a result,
the average consumer in a small market loses $.83 per month, whereas the
average consumer in a large market loses $.37 per month. These losses
are equivalent to about $45 million annually for all small-market
households in the United States and $ 13 million for all large-market
households. (16) If the change in market structure occurs in all
markets, for example, if two of the big four networks ABC, CBS, FOX, or
NBC consolidated, annual aggregate losses nationwide would be about $680
million. For comparison, this represents about 7% of the total operating
costs for CB S in 2010. (17)
Given the mean WTPs in Table 6, it is not surprising that the
average welfare losses per month from DIVERSITY OF OPINION and COMMUNITY
NEWS are greater than MULTICULTURALISM in almost all markets. However,
while DIVERSITY OF OPINION continues to have significant negative
impacts in both small (-$.55) and large (-$.36) markets, the effect for
COMMUNITY NEWS quickly dissipates from -$.43 to -$.08 as the number of
stations in the market increases. MULTICULTURACISM follows a similar
trend to DIVERSITY OF OPINION, losing about 30% of its negative impact
from small (-$.19) to large (-$.13) markets. ADVERTISING also follows a
similar trend to DIVERSITY OF OPINION and MULTICUETURALISM losing about
40% of its positive impact from small ($.34) to large ($.20) markets.
An interesting observation is the potential tradeoff between the
amount of diversity and localism in news service, and the amount of
space and time devoted to advertising. Consumers lose from the
consolidation of two independent TV stations because there is less
diversity of opinion, less coverage of multiculturalism issues, and less
community news, but they gain because there is less space and time
devoted to advertising. (18) Specifically, as seen in columns 3 through
6 of Table 8, on average, about 31% of the annual monthly losses to
consumers from less diversity and localism in each market are offset by
less exposure to advertising. This illustrates an important feature of
the news service experience in our data; the first-order effects from
consolidation are, potentially, not all bad for consumers. Nevertheless,
consumers and policy makers should be concerned about the impacts from a
"virtual merger" where TV stations combine their news
operations with joint operating and marketing agreements without
actually merging. Because a virtual merger is likely to result in less
diversity and localism but not less advertising, the welfare reductions
in Table 8 would be even more pronounced. For example, column 12 shows
that if the virtual merger occurred in all markets, annual aggregate
losses nationwide would be about $992 million. (19)
[FIGURE 1 OMITTED]
VI. CONCLUSIONS
This study examined market structure and media diversity. A mixed
logit model was used to estimate consumer demand for their local news
service, described by the offerings from newspapers, radio, TV, the
Internet, and Smartphone. The demand estimates are used to conduct a
policy experiment that calculates the impact on consumer welfare from a
marginal decrease in the number of independent TV stations that lowers
the amount of diversity, localism, and advertising in the market. Our
results show that consumer welfare decreases following the change in
media market structure, and that the losses are smaller in large
markets.
We make no claims as to whether media ownership rules should be
relaxed or tightened. Additionally, we do not measure the consumer
welfare effects from media outlet cross-ownership, but note that this
could be a fruitful avenue of future research. We do note that the
estimated total loss of $681 million approximates the extreme case of
consolidation between two major national media players and, as such, is
an upper-bound calculation. The large consumer loss in small TV markets
relative to large markets is potentially important. The tradeoff between
diversity and localism, and advertising, is also interesting because it
highlights an additional benefit for consideration during the analysis
of a media market merger. It also provides a new policy angle from which
to assess the efficacy of media ownership rules.
ABBREVIATIONS
FCC: Federal Communications Commission
TWC: Time Warner Cable
WTP: Willingness-to-Pay
doi:10.1111/ecin.12153
REFERENCES
Brown, K., and P. Alexander. "Market Structure, Viewer
Welfare, and Advertising Rates in Local Broadcast Markets."
Economic Letters, 86(3), 2004, 331-37.
Brownstone, D., and K. Train. "Forecasting New Product
Penetration with Flexible Substitution Patterns." Journal of
Econometrics, 89(1), 1999, 109-29.
Crawford, G. "Television Station Ownership Structure and the
Quantity and Quality of TV Programming," FCC Media Ownership Study
#3, FCC, Washington, DC. 2007.
Cunningham, B" and P. Alexander. "A Theory of Broadcast
Media Concentration and Commercial Advertising." Journal of Public
Economic Theory, 6(4), 2004, 557-75.
Domberger, S., and A. Sherr. "The Impact of Competition on
Pricing and Quality of Legal Services." International Review of Law
and Economics, 9, 1989, 41-56. Drushel, B. "The Telecommunications
Act of 1996 and Radio Market Structure." Journal of Media
Economics, 11(3), 1998, 3-20.
FCC. "Government Furnished Information on Media Market
Structure." February 17, Media Bureau, FCC, Washington, DC, 2011.
Gentzkow, M. "Valuing New Goods in a Model with
Complementarity: Online Newspapers." American Economic Review,
97(3), 2007, 713-44.
Gentzkow, M., and J. Shapiro. "What Drives Media Slant?
Evidence from U.S. Daily Newspapers." Econometrica, 78(1). 2010.
35-71.
Goolsbee, A., and A. Petrin. "The Consumer Gains from Direct
Broadcast Satellites and the Competition with Cable TV."
Econometrica, 72(2), 2004, 351-81.
Greenstein, S., and F. Zhu. "Is Wikipedia Biased?"
American Economic Review Papers & Proceedings, 102(3), 2012, 343-48.
Groseclose.'T., and J. Milyo. "A Measure of Media
Bias." Quarterly Journal of Economics, 120(4), 2005, 1191-237.
Heckman, J. "Sample Selection Bias as a Specification
Error." Econometrica, 47(1), 1979, 153-61.
Huber, J., and K. Zwerina. "The Importance of Utility Balance
in Efficient Choice Designs." Journal of Marketing Research, 33,
1996, 307-17.
Imbens, G., and J. Wooldridge. "Control Function and Related
Methods." Lecture Notes 6 for What's New in Econometrics?
NBER, 2007.
Ivaldi, M., and F. Verboven. "Quantifying the Effects from
Horizontal Mergers in European Competition Policy." International
Journal of Industrial Organization, 23(9/10), 2005, 669-91.
Knowledge Networks Inc. "KnowledgePanel[R] Demographic Profile
January 2010." Government and Academic Research, Knowledge
Networks, 2010.
Manuszak, M., and C. Moul. "Prices and Endogenous Market
Structure in Office Supply Superstores." Journal of Industrial
Economics, 56, 2008, 94-112.
Matsa, D. "Competition and Product Quality in the Supermarket
Industry." Quarterly Journal of Economics, 126(3), 2011. 1539-91.
Mazzeo. M. "Competitive Outcomes in Product-Differentiated
Oligopoly." Review of Economics and Statistics, 84(4), 2002,
716-28.
--. "Competition and Service Quality in the US Airline
Industry." Review of Industrial Organization, 22(4), 2003, 275-96.
McKelvey, R., and W. Zavoina. "A Statistical Model for the
Analysis of Ordinal Level Dependent Variables." Journal of
Mathematical Sociology 4(1), 1975, 103-20.
Milyo, J. "The Effects of Cross-Ownership on the Local Content
and Political Slant of Local Television News." FCC Media Ownership
Study #6, FCC, Washington, DC, 2007.
National Climatic Data Center. Climate Data Online. Washington, DC:
U.S. Department of Commerce, 2011. Accessed September 22, 2011.
http://www.ncdc.noaa. gov/cdo-web/.
Nevo, A. "Mergers with Differentiated Products: The Case of
the Ready to Eat Cereal Industry." Rand Journal of Economics,
31(3), 2000, 395-421.
Olivares, M., and G. Cachon. "Competing Retailers and
Inventory: An Empirical Investigation of General Motors'
Dealerships in Isolated U.S. Markets." Management Science, 55(9),
2009, 1586-604.
Owen, B., and S. Wildman. Video Economics. Cambridge, MA: Harvard
University Press, 1992.
Pinske, J., and M. Slade. "Mergers, Brand Competition and the
Price of a Pint." European Economic Review, 48(3), 2004,617-43.
Revelt, D., and K. Train. "Mixed Logit with Repeated Choices:
Households' Choices of Appliance Efficiency Level." Review of
Economics and Statistics, 80(4), 1998, 647-57.
Siegelman, P., and J. Waldfogel. "Race and Radio: Preference
Externalities, Minority Ownership, and the Provision of Programming to
Minorities," in Advances in Applied Microeconomics. Advertising and
Differentiated Products, Vol. 10, edited by M. Baye and J. Nelson.
Amsterdam: Elsevier, 2001.
Singh, V., and T. Zhu. "Pricing and Market Concentration in
Oligopoly Markets." Marketing Science, 27(6), 2008, 1020-35.
Train, K., and W. Wilson. "Estimation on Stated-Preference
Experiments Constructed from Revealed-Preference Choices."
Transportation Research Part B: Methodological, 42(3), 2008, 191-203.
United States Census Bureau. American Factfinder. Washington, DC:
United States Census Bureau, 2009.
Yan, M., and P. Napoli. "Market Competition, Station
Ownership, and Local Public Affairs Programming on Broadcast
Television." Journal of Communications, 56(1), 2006, 795-812.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
APPENDIX S1. Online Appendix.
(1.) For example, Owen and Wildman (1992) argue that economies of
scale in program distribution support the supply of non-local content.
Given a fixed cost of producing news content, multiple station owners
can spread these costs over more stations by distributing the same,
non-locally oriented content across many communities.
(2.) The two constructed alternatives provide information on news
and current affairs from the same set of media sources indicated by the
respondent during cognitive buildup, but differing by the levels of the
four defined non-price characteristics, diversity of opinion,
multiculturalism, community news, advertising, and prices. Figure 1A in
Appendix S1 provides an example choice scenario.
(3.) Our use of stated and revealed preference data is similar to
the pivoting approach described by Train and Wilson (2008). The
advantage of this approach is that it brings more realism to the choice
experiments. However, it also requires that consumers hold all features
constant during their A or B versus status quo choice. For robustness,
we estimated Equation (2) on A versus B choice data only and compared it
to estimates on the A or B versus status quo choice data. The results
indicated in Table A5 Appendix S1 show a qualitatively similar pattern
of preferences across the two data sources with WTP estimates from the A
versus B choice data being slightly attenuated. We also estimated a
model with a constant for the status quo service that varies across
consumers. The results, not reported, are qualitatively similar to those
in Table 5 and suggest that endogeneity from omitted variable bias in
the status quo choice is minor.
(4.) The discrete-choice model actually estimates ala and
[b.sub.1]/[sigma], where [sigma] is the scale parameter. The WTP
calculation is not affected by the presence of the scale parameter
because -([b.sub.1]/[sigma])/([alpha]/[sigma]) = -[b.sub.1]/[alpha].
(5.) The table of selected demographic characteristics, probit
model estimates, and the procedures used to develop the
post-stratification weights is available from the authors upon request.
(6.) Our estimates of preferences, reported in Table 5, indicate
that consumers generally want more diversity in the reporting of news
and information, more local news, and more coverage of minority issues.
Holding prices and the number of media outlets constant, one would
expect high media consumption in markets with survey responses
indicating higher levels of these news service characteristics. As an
"honesty check" for the quality of survey responses, we
regressed household media consumption (i.e., the number of media sources
consumed on a regular basis) on DIVERSITY OF OPINION, MULTICULTURALISM,
and COMMUNITY NEWS, and controls for the number of media outlets and
prices. The results, not reported, show that the number of media sources
consumed is positively and significantly correlated with DIVERSITY OF
OPINION, MULTICULTURALISM, and COMMUNITY NEWS.
(7.) Television market area or "market" is a region where
all households receive the same offerings from TV stations. The seven
markets outside our sample are: Bend, OR; Fairbanks, AK; Grand Junction,
CO; Missoula, MT; North Platte, NE; Ottumwa, IA; Kirksville, MO; and
Presque, ME. All are small markets with five or fewer TV stations. As
shown in Table 3, the remaining small markets in our sample cover 8.43%
of households. FCC (2011) data show that 8.37% of population households
were in small markets at December, 2009.
(8.) All mixed logit models were estimated by simulated maximum
likelihood using 100 Halton draws. For robustness, we estimated several
model specifications using 500 draws and the results are similar. The
estimated variance-covariance matrices from the correlated random
parameters specifications are available in Table A7 of Appendix S1.
(9.) Table 6 presents the mean WTPs for low-, middle-, and
high-income groups, respectively. In the policy experiment in Section V,
WTPs are simulated for each individual respondent.
(10.) For robustness, we divided our data into subsamples based on
respondent's income and estimated a mixed logit model on each
subsample separately. The results indicated in Table A6 of Appendix SI
show very little difference in WTP between the medium- and high-income
groups, and small differences in the WTP for advertising and
multiculturalism in the low-income group.
(11.) These demographics are common controls in demand and supply
models of telecommunications. For robustness, we estimated alternative
specifications of the ordered probit model with dummy variables
representing the different levels for each of the demographic variables.
The results for the important relationships, i.e., the effects of a
change in VOICES on the estimated supply probabilities for DIVERSITY OF
OPINION, MULTICULTURALISM, COMMUNITY NEWS, and ADVERTISING, are
essentially unchanged.
(12.) Since the fixed cost variables. FREEZE and SNOW x FREEZE, in
the first step affect latent profits, but not the supply of news service
characteristics. Equation (4) is identified. A likelihood ratio tests
reject the null that the estimated coefficients on the excluded
variables, FREEZE and SNOW x FREEZE, in the ordered probit model of
latent profits are jointly equal to zero at the 1 % significance level
(12.5 > [chi square](3) = 9.21).
(13.) By definition, a reduction in the number of independent TV
stations means there are fewer viewpoints in the market, and as a
result, less diversity of opinion.
(14.) A referee noted concern about the differing signs of
coefficients on income in Table 7. The ordered probit results in Table 7
are reduced-form estimates of the supply of news characteristics. The
estimated coefficients for the control variables (Education, Income,
etch have no clear a priori signs and can be different in the ordered
probit estimates of the four features.
(15.) The reported standard errors are calculated using a
bootstrapping method. For example, in row 1, we construct the benefit
Equation (7) for each respondent in markets with five TV stations. We
then draw marginal utility values from the multivariate normal
distribution implied by the mean and covariance parameters reported in
columns 8 and 9 of Table 4. These values are used to evaluate Equation
(7) for each household respondent and to obtain an estimate of the mean
consumer welfare effect per month. We run this simulation 500 times and
report the mean and standard error of the sampling distribution for
consumer welfare per month.
(16.) There are 90,193,905 population households in markets from 5
to 20 TV stations. Total annual aggregate consumer welfare for small
markets is -$45.07 million = (0.050 x 90,193,905) x 12 x -0.83. Total
annual aggregate consumer welfare for large markets is -$15.51 million =
(0.032 x 90,193,905) x 12 x -0.44.
(17.) See CBS Corporation income statements at
http://ycharts.com/financials/CBS/income_statement/annual.
(18.) The reduction in advertising does not mean that that the two
merged firms will be worth less. Profits are expected to increase as a
result of higher advertising rates and/or cost efficiencies in the
production of news.
(19.) The FCC uses several measures of market structure when
discussing ownership rules. For robustness, we examined how sensitive
our results are to an alternative specification of the media supply
Equation (4) that controls for the number of daily newspapers in the
market (NEWSPAPERS) and the number of radio stations (RADIO STATIONS).
Estimates of the ordered probit model of media supply, and the estimates
of the impacts on consumer welfare from a change in market structure,
not reported here, are similar to those presented in Tables 7 and 8.
R. SCOTT HILLER, SCOTT J. SAVAGE and DONALD M. WALDMAN *
* The Federal Communications Commission (FCC) and the Time Warner
Cable (TWC) Research Program on Digital Communications provided funding
for this research. We are grateful to Jessica Almond, Fernando Laguarda,
Jonathan Levy, and Tracy Waldon for their assistance during the
completion of this project. Jonathan Baker, Yongmin Chen, Nicholas
Flores, Shane Greenstein, Robert Innes, Jin-Hyuk Kim, David Layton,
Edward Morey, Gregory Rosston, Joel Waldfogel, Spencer Waller, Bradley
Wimmer, seminar participants at the University of California, Merced,
University of Colorado, Boulder, Northwestern University, SUNY, Stony
Brook, attendees at the Conference in Ffonor of Professor Emeritus
Lester D. Taylor at Jackson Hole, the editor and two anonymous referees
provided helpful comments and contributions. Any opinions expressed here
are those of the authors and not those of the FCC or TWC.
Hiller: Department of Economics, Fairfield University, 1073 N.
Benson Rd., Fairfield, CT 06824. Phone 203-254-4000 ext. 2795, Fax
203-254-4074, E-mail rhiller@fairfield.edu
Savage: Department of Economics, University of Colorado Boulder.
Campus Box 256, Boulder, CO 80309. Phone 303-735-1165, Fax 303-492-1112,
E-mail scott. savage@colorado.edu
Waldman: Department of Economics, University of Colorado Boulder.
Campus Box 256. Boulder, CO 80309. Phone 303-492-8960, Fax 303-492-1112,
E-mail donald.waldman@colorado.edu
TABLE 1
News Service Characteristics
Characteristic Levels
COST (p) The total of monthly
subscriptions to all of the
household's media sources, plus
any contributions to public radio
or public TV stations (ranging
from $0 to $250 per month).
DIVERSITY OF OPINION ([x.sub.1]) The extent to which the
information on news and current
affairs in the household's
overall media environment
reflects different viewpoints.
Low: only one viewpoint.
Medium: a few different
viewpoints.
High: many different
viewpoints.
COMMUNITY NEWS ([x.sub.2]) The amount of information on
community news and events in the
household's overall media
environment.
Low: very little or no
information on community
news and events.
Medium: some information on
community news and events.
High: much information on
community news and events.
MULTICULTURALISM ([x.sub.3]) The amount of information on news
and current affairs in the
household's overall media
environment that reflects the
interests of women and
minorities.
Low: very little or no
information reflecting
the interests of women
and minorities.
Medium: some information
reflecting the interests
of women and minorities.
High: much information
reflecting the interests
of women and minorities.
ADVERTISING ([x.sub.4]) The amount of space and/or time
devoted to advertising in the
household's overall media
environment.
Low: barely noticeable.
Medium: noticeable but not
annoying.
High: annoying.
Notes: The upper limit of $250 per month for COST is the total cost
for a media environment with a 7-day subscription to a premium
newspaper, such as the New York Times or San Francisco Chronicle
($25), a "All of XM" subscription to satellite radio ($20), a premier
subscription to cable or satellite television ($110), a subscription
to very-fast Internet service ($45), an unlimited data subscription
for a Smartphone ($30), and $10 monthly memberships to both NPR and
PBS. Detailed descriptions of the characteristics as they appeared in
the survey questionnaire are available from the authors.
TABLE 2
Summary Statistics for News Service Characteristics
Feature Obs. Mean SD Min Max
DIVERSITY OF OPINION 5,131 2.09 .655 1 3
COMMUNITY NEWS 5,131 1.99 .711 1 3
MULTICULTURALISM 5,131 1.83 .705 1 3
ADVERTISING 5,131 2.29 .682 1 3
COST ($ per month) 5,131 111.2 76.03 0 447
CONTRIBUTION ($ annual) 535 111.5 161.5 .25 1,500
BUNDLE 3,688 .576 .494 0 1
Notes: 1 = "low", 2 = "medium," and 3 = "high" for DIVERSITY OF
OPINION, COMMUNITY NEWS, MULTICULTURALISM, and ADVERTISING.
CONTRIBUTION is value of contributions to public radio and public TV
stations during the past 12 months. BUNDLE = 1 when subscription
television service is bundled with Internet service and/or other
telephone services. Obs., number of observations; SD, standard
deviation; Min, minimum value; Max, maximum value.
TABLE 3
Media Market Structure
Variable Description
HOUSEHOLDS Number of households in the market.
NEWSPAPERS Number of daily newspapers with a city of
publication located in a county in the
market.
RADIO STATIONS Number of radio stations in the market.
STATIONS Number of full-power TV stations in the market.
MEDIA OUTLETS NEWSPAPERS plus RADIO STATIONS plus STATIONS.
NEWSPAPER VOICES Number of parent entities owning a daily
newspaper in the market.
RADIO VOICES Number of independent radio voices in the
market.
VOICES Number of independent TV voices in the market.
MEDIA VOICES NEWSPAPER VOICES plus RADIO VOICES plus VOICES.
TV-NEWSPAPER VOICES Number of independent newspaper and TV voices
in the market.
TV-RADIO VOICES Number of independent radio and TV voices in
the market.
Source: FCC (2011).
TABLE 4
Summary Statistics for Media Market Structure
Variable Markets Mean SD Min
All markets
HOUSEHOLDS 203 1,670,158 1,842,396 4,145
SMALL MARKETS 203 .084 .278 0
MEDIA OUTLETS 203 138.7 71.25 4
MEDIA VOICES 203 73.11 35.97 3
NEWSPAPERS 203 12.76 8.206 0
RADIO STATIONS 203 113.2 59.41 3
STATIONS 203 12.74 5.879 1
NEWSPAPER VOICES 203 7.634 4.076 0
RADIO VOICES 203 55.12 28.75 2
VOICES 203 10.36 4.626 1
TV-NEWSPAPER VOICES 203 11.91 4.758 1
TV-RADIO VOICES 203 63.06 30.95 2
Small markets (five or fewer TV stations)
HOUSEHOLDS 68 195,814 98,806 4,145
MEDIA OUTLETS 68 46.97 15.90 4
MEDIA VOICES 68 26.36 8.695 3
NEWSPAPERS 68 4.160 2.347 0
RADIO STATIONS 68 38.60 13.85 3
STATIONS 68 4.211 1.060 1
NEWSPAPER VOICES 68 3.308 1.900 0
RADIO VOICES 68 19.00 6.608 2
VOICES 68 4.046 1.097 1
TV-NEWSPAPER VOICES 68 5.734 1.302 1
TV-RADIO VOICES 68 22.54 7.316 2
Variable 25th 75th Max
All markets
HOUSEHOLDS 447,396 2,228,143 7,444,659
SMALL MARKETS n.a. n.a. 1
MEDIA OUTLETS 80 182 291
MEDIA VOICES 44 97 152
NEWSPAPERS 6 19 32
RADIO STATIONS 64 157 241
STATIONS 8 17 27
NEWSPAPER VOICES 4 10 19
RADIO VOICES 31 73 119
VOICES 7 13 22
TV-NEWSPAPER VOICES 8 15 24
TV-RADIO VOICES 38 85 129
Small markets (five or fewer TV stations)
HOUSEHOLDS 116,273 264,844 395,620
MEDIA OUTLETS 37 57 86
MEDIA VOICES 20 34 41
NEWSPAPERS 2 6 11
RADIO STATIONS 30 48 75
STATIONS 4 5 5
NEWSPAPER VOICES 2 4 8
RADIO VOICES 14 25 31
VOICES 3 5 5
TV-NEWSPAPER VOICES 5 7 8
TV-RADIO VOICES 17 28 35
Note: Markets is the number of television markets.
SD, standard deviation; Min, minimum value; Max, maximum value; 25th,
25th percentile; 75th, 75th percentile; n.a., not applicable.
Source: FCC (2011).
TABLE 5
Mixed Logit Estimates of the Demand for Local News Service
Model (1) Model (2)
Mean Mean SD
COST -.020 *** -.028 ***
(.0002) (.0003)
COST x MED_INCOME
COST x HIGH_INCOME
DIVERSITY OF OPINION .383 *** .433 *** .810 ***
(.009) (.016) (.019)
COMMUNITY NEWS .461 *** .433 *** .649 ***
(.009) (.014) (.018)
MULTICULTURALISM .012 .015 .685 ***
(.009) (.015) (.019)
ADVERTISING -.357 *** -.227 *** .695 ***
(.010) (.016) (.019)
MEDIUM DIVERSITY OF
OPINION
HIGH DIVERSITY OF
OPINION
MEDIUM COMMUNITY NEWS
HIGH COMMUNITY NEWS
MEDIUM
MULTICULTURALISM
HIGH MULTICULTURALISM
MEDIUM ADVERTISING
HIGH ADVERTISING
ALTERNATIVE SPECIFIC .769 *** .911 ***
CONSTANT
(.018) (.021)
Likelihood -59,453 -32,714
Model (3) Model (4)
Mean SD Mean SD
COST -.028 *** -.037 ***
(.0003) (.001)
COST x MED_INCOME .004 ***
(.001)
COST x HIGH_INCOME .011 ***
(.001)
DIVERSITY OF OPINION .448 *** .805 *** .443 *** .801 ***
(.016) (.019) (.016) (.019)
COMMUNITY NEWS .450 *** .591 .449 *** .590 ***
(.014) (.019) (.014) (.019)
MULTICULTURALISM .041 *** .599 *** .041 *** .604 ***
(.015) (.021) (.015) (.021)
ADVERTISING -.229 *** .692 *** -.244 *** .681 **
(.016) (.020) (.016) (.020)
MEDIUM DIVERSITY OF
OPINION
HIGH DIVERSITY OF
OPINION
MEDIUM COMMUNITY NEWS
HIGH COMMUNITY NEWS
MEDIUM
MULTICULTURALISM
HIGH MULTICULTURALISM
MEDIUM ADVERTISING
HIGH ADVERTISING
ALTERNATIVE SPECIFIC .888 *** .888 ***
CONSTANT
(.021) (.022)
Likelihood -32,477 -32,303
Model (5)
Mean SD
COST -.042 ***
(.001)
COST x MED_INCOME .005 ***
(.001)
COST x HIGH_INCOME .013 ***
(.001)
DIVERSITY OF OPINION
COMMUNITY NEWS
MULTICULTURALISM
ADVERTISING
MEDIUM DIVERSITY OF .748 *** 1.295 ***
OPINION
(.032) (.043)
HIGH DIVERSITY OF 991 *** 1.076 ***
OPINION (.037) (.043)
MEDIUM COMMUNITY NEWS .894 *** 1.197 ***
(.033) (.045)
HIGH COMMUNITY NEWS 1.116 *** .510 ***
(.034) (.072)
MEDIUM .253 *** .473 ***
MULTICULTURALISM
(.027) (.075)
HIGH MULTICULTURALISM .147 *** .159 ***
(.032) (.062)
MEDIUM ADVERTISING -.169 *** -.029 ***
(.023) (.049)
HIGH ADVERTISING -.739 *** .291 ***
(.039) (.081)
ALTERNATIVE SPECIFIC .816 ***
CONSTANT
(.026)
Likelihood -30,956
Notes: Estimated by simulated weighted maximum likelihood. Model (1)
is estimated with the conditional logit model. Models (2) through (5)
are estimated with the mixed logit model. Mean and SD are the
estimated means and standard deviations of the random marginal utility
parameters. Covariances of correlated random parameters in models (3)
through (5) are available in Table A7 of Appendix SI. ALTERNATIVE
SPECIFIC CONSTANT equals one for actual news service alternative at
home and zero for news service alternatives A and B. Robust standard
errors in parentheses. Number of observations is 40,816.
*** denotes significant at the 1% level. ** denotes significant at the
5% level, * denotes significant at the 10% level.
TABLE 6
Mean WTP by Household Income
Low income $25,000 [less than
<$25,000 or equal to] Medium
income < $50,000
Linear preferences
DIVERSITY OF OPINION $12.24 $13.74
(.69) (.78)
COMMUNITY NEWS $12.41 $13.95
(.58) (.64)
MULTICULTURALISM $1.15 $1.27
(.61) (.67)
ADVERTISING $(6.75) $(7.56)
(.60) (.66)
Nonlinear preferences
MEDIUM DIVERSITY OF OPINION $18.16 $20.89
(1.01) (1.12)
HIGH DIVERSITY OF OPINION $24.08 $27.67
(1.36) (1.54)
MEDIUM COMMUNITY NEWS $21.74 $24.92
(1.06) (1.24)
HIGH COMMUNITY NEWS $27.17 $31.09
(1.22) (1.40)
MEDIUM MULTICULTURALISM $6.15 $7.05
(.72) (.83)
HIGH MULTICULTURALISM $3.60 $4.06
(1.11) (1.25)
MEDIUM ADVERTISING $(4.13) $(4.72)
(.47) (.54)
HIGH ADVERTISING $(17.97) $(20.66)
(1.38) (1.56)
$50,000 [less
than or equal
to] High income
Linear preferences
DIVERSITY OF OPINION $17.79
(1.01)
COMMUNITY NEWS $18.03
(.85)
MULTICULTURALISM $1.64
(.87)
ADVERTISING $(9.78)
(.87)
Nonlinear preferences
MEDIUM DIVERSITY OF OPINION $26.38
(1.43)
HIGH DIVERSITY OF OPINION $34.99
(2.01)
MEDIUM COMMUNITY NEWS $31.53
(1.51)
HIGH COMMUNITY NEWS $39.38
(1.71)
MEDIUM MULTICULTURALISM $8.92
(1.02)
HIGH MULTICULTURALISM $5.22
(1.65)
MEDIUM ADVERTISING $(5.98)
(.68)
HIGH ADVERTISING $(26.07)
(1.94)
Notes: Bootstrapped standard errors are in parentheses.
Willingness-to-pay is calculated using the mean of each of the
random marginal utility parameters and the marginal disutility
of COST. The marginal disutility of COST varies by household
income and is [[beta].sub.1] + [[beta].sub.M]MED_INCOME +
[[beta].sub.H]HIGH_INCOME, where MED_INCOME equals one when
household income is greater than $25,000 and less than
$50,000 and zero otherwise, and HIGH_INCOME equals one when household
income is greater than $50,000 and zero otherwise. Linear calculations
use utility estimates from model (4) in Table 4. Nonlinear
calculations use utility estimates from model (5) in Table 4. The
parentheses on MEDIUM ADVERTISING indicate WTP to move from a medium
to a low level of advertising. The parentheses on HIGH ADVERTISING
indicate WTP to move from a high to a low level of advertising.
TABLE 7
Second-Step Ordered Probit Estimates of Relationship between News
Service Supply and Market Structure
DIVERSITY OF OPINION COMMUNITY NEWS MULTICULTURALISM ADVERTISING
VOICES .0682 *** .0463 *
(.0257) (.0275)
VOICES x STATIONS -.0001 -.0017 **
(.0009) (.0007)
STATIONS -.0124 .0023
(.0173) (.0166)
AGE .0409 *** .0828 ***
(.0098) (.0165)
EDUC .1420 *** .0828 ***
(.0185) (.0165)
GENDER -.0412 .1310 ***
(.0327) (.0284)
INCOME .0595 *** -.0013
(.0128) (.0144)
RACE .0582 *** -.1210 ***
(.0334) (.0382)
[[??].sub.m] -.0602 *** -.0256 *
(.0260) (.0173)
Likelihood -4,844.4 -5,218.4
Mean change in
predicted probabilities
[DELTA][P.sub.L]/[DELTA]X .0142 .0077
[DELTA][P.sub.M]/[DELTA]X .0028 -.0003
[DELTA][P.sub.H]/[DELTA]X -.0170 -.0074
VOICES .0941 ** .0497 **
(.0253) (.0242)
VOICES x STATIONS -.0012 -.0011
(.0008) (.0007)
STATIONS -.0311 ** -.0008
(.0155) (.0147)
AGE .0263 ** .1110 ***
(.0104) (.0112)
EDUC .1250 *** .0998 ***
(.0152) (.0208)
GENDER .0516 .0188
(.0357) (.0260)
INCOME -.0275 ** .0295 **
(.0139) (.0121)
RACE -.0836 ** .2210 ***
(.0408) (.0446)
[[??].sub.m] -.0414 -.0194
(.0257) (.0159)
Likelihood -5.182.8 -4,910.8
Mean change in
predicted probabilities
[DELTA][P.sub.L]/[DELTA]X .0289 .0076
[DELTA][P.sub.M]/[DELTA]X -.0096 .0060
[DELTA][P.sub.H]/[DELTA]X -.0193 -.0136
Notes: Estimated by weighted maximum likelihood. Bootstrapped standard
errors in parentheses. Estimated cutoff parameters and estimated
parameters for the media alternative dummy variables are not reported.
Number of observations is 5,102. Sample mean probabilities are
calculated from each individual respondent's predicted probabilities.
[DELTA][P.sub.L] = [P.sub.L1] -[P.sub.L0], [DELTA][P.sub.M] =
[P.sub.M1] -[P.sub.M0], [DELTA][P.sub.M] = [P.sub.H1] -[P.sub.M0], and
[DELTA]X = [DELTA]VOICES = 1. [[??].sub.m] is the modified error
correction term calculated from the parameter estimates from the
first-step profit equation.
*** denotes significant at the 1% level, ** denotes significant at the
5% level, * denotes significant at the 10% level.
TABLE 8
Impact on Consumer Welfare from a Change in Market Structure
Average Consumer Welfare per
Month (Dollars per Month)
Size Pop. Share DIV MCULT ADV CNEWS
5 .050 -.55 -.19 .34 -.43
(.05) (.06) (.04) (.03)
6 .061 -.53 -.19 .33 -.39
(.06) (.07) (.05) (.03)
7 .091 -.54 -.20 .33 -.38
(.05) (.06) (.05) (.03)
8 .081 -.51 -.18 .32 -.35
(.05) (.06) (.05) (.03)
9 .095 -.49 -.17 .30 -.33
(.05) (.06) (.05) (.02)
10 .056 -.49 -.18 .30 -.31
(.06) (.07) (.05) (.03)
11 .099 -.48 -.17 .29 -.30
(.04) (.05) (.04) (.02)
12 .069 -.47 -.18 .28 -.28
(.05) (.06) (.04) (.02)
13 .024 -.46 -.16 .28 -.26
(.08) (.10) (.06) (.03)
14 .093 -.43 -.14 .27 -.24
(.04) (.06) (.04) (.02)
15 .03 -.43 -.15 .25 -.22
(.08) (.09) (.06) (.03)
16 .079 -.40 -.14 .26 -.20
(.05) (.06) (.04) (.02)
17 .072 -.41 -.14 .24 -.18
(.05) (.07) (.04) (.02)
18 .043 -.40 -.14 .24 -.17
(.06) (.08) (.05) (.02)
19 .026 -.40 -.15 .24 -.15
(.07) (.10) (.06) (.02)
20 .032 -.36 -.13 .20 -.08
(.03) (.04) (.02) (.01)
Total 1 -.47 -.17 .29 -.28
(.01) (.01) (.01) (.01)
Annual Aggregate Welfare in Market
(Dollars in Millions)
Size Pop. Share DIV MCULT ADV CNEWS
5 .050 -29.65 -10.50 18.21 -23.13
(2.61) (3.15) (2.42) (1.57)
6 .061 -35.15 -12.33 21.56 -25.75
(4.05) (4.90) (3.54) (2.24)
7 .091 -52.72 -19.50 32.13 -37.10
(4.78) (6.07) (4.48) (2.51)
8 .081 -44.71 -15.73 27.95 -30.62
(4.33) (5.23) (4.01) (2.27)
9 .095 -50.04 -17.74 31.34 -33.64
(5.10) (6.14) (4.70) (2.57)
10 .056 -29.72 -10.62 18.34 -19.09
(3.73) (4.35) (3.09) (1.73)
11 .099 -51.70 -18.67 31.59 -31.80
(4.36) (5.57) (3.99) (2.14)
12 .069 -35.22 -13.16 20.90 -20.69
(3.88) (4.68) (3.35) (1.68)
13 .024 -11.90 -4.21 7.21 -6.72
(1.95) (2.52) (1.62) (.83)
14 .093 -42.81 -14.30 27.33 -23.74
(4.34) (5.63) (3.65) (1.74)
15 .03 -14.01 -4.83 8.24 -7.08
(2.68) (3.08) (2.11) (.90)
16 .079 -34.63 -11.77 22.58 -17.50
(4.14) (5.43) (3.46) (1.35)
17 .072 -31.60 -11.01 18.97 -14.32
(3.65) (5.11) (3.22) (1.30)
18 .043 -18.62 -6.72 11.06 -7.87
(2.99) (3.81) (2.25) (.88)
19 .026 -11.12 -4.10 6.72 -4.34
(2.08) (2.73) (1.75) (.55)
20 .032 -12.53 -4.52 6.93 -2.70
(1.01) (1.26) (.70) (.18)
Total 1 -506.12 -179.73 311.06 -306.08
(15.35) (15.35) (10.46) (6.82)
Annual Aggregate
Welfare in Market
(Dollars in Millions)
Size Pop. Share Total Total less ADV
5 .050 -45.07 -63.28
6 .061 -51.67 -73.23
7 .091 -77.19 -109.32
8 .081 -63.12 -91.07
9 .095 -70.09 -101.42
10 .056 -41.09 -59.43
11 .099 -70.57 -102.16
12 .069 -48.17 -69.07
13 .024 -15.62 -22.83
14 .093 -53.52 -80.85
15 .03 -17.68 -25.92
16 .079 -41.31 -63.90
17 .072 -37.95 -56.93
18 .043 -22.15 -33.20
19 .026 -12.84 -19.57
20 .032 -12.81 -19.74
Total 1 -680.87 -991.93
Notes: Bootstrapped standard errors in parentheses. The change in
market structure is a one-unit reduction in the number of independent
TV voices in the market, all other things held constant. There are
90,193,905 population households in markets from 5 to 20 TV stations
(FCC 2011). Pop. share is the number of population households in the
market divided by population households. DIV is diversity of opinion
in the reporting of information, MCULT is coverage of multiculturalism
issues, ADV is amount of space or time devoted to advertising, and
CNEWS is amount of information on community news and events. Total
loss of $832.1 million is the sum of the individual market losses.
