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  • 标题:Market structure and media diversity.
  • 作者:Hiller, R. Scott ; Savage, Scott J. ; Waldman, Donald M.
  • 期刊名称:Economic Inquiry
  • 印刷版ISSN:0095-2583
  • 出版年度:2015
  • 期号:April
  • 语种:English
  • 出版社:Western Economic Association International
  • 摘要: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.
  • 关键词:Advertising;Cable television broadcasting industry;Market research services;Marketing research firms;Multiculturalism

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.
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