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Turn On, Tune In, Drop Out: RadioListening, Ownership Policy, andTechnologyCatherine Tyler Mooney aa Department of Economics , University of OklahomaPublished online: 29 Nov 2010.

To cite this article: Catherine Tyler Mooney (2010) Turn On, Tune In, Drop Out: RadioListening, Ownership Policy, and Technology, Journal of Media Economics, 23:4, 231-248, DOI:10.1080/08997764.2010.527229

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Journal of Media Economics, 23:231–248, 2010

Copyright © Taylor & Francis Group, LLC

ISSN: 0899-7764 print/1532-7736 online

DOI: 10.1080/08997764.2010.527229

Turn On, Tune In, Drop Out: Radio Listening,Ownership Policy, and Technology

Catherine Tyler MooneyDepartment of Economics

University of Oklahoma

Radio listening in the United States fell by more than 10% between 1998 and 2003. During

this time, broadcast radio faced new competition from satellite radio and the Internet while the

industry was also undergoing significant changes due to increased radio ownership caps. This article

quantifies the effects of these factors on audience sizes and explores the implications for audience

composition and programming content. The results show that industry consolidation played a larger

role in decreasing overall listening than new technology. New technology did have a role in altering

the distribution of listeners among programming formats.

From 1997 to 2003 the percentage of the U.S. population listening to commercial radio at any

given time fell by 9% in the average metropolitan area. Similar trends have developed in other

traditional media, especially newspapers; and, to a lesser extent, television. The commonly

blamed culprit is new technology, especially the Internet. However, the decline in listening

began well before the Internet became widespread. Declines in listening correspond more

closely with changes in radio station ownership.The average Internet penetration rate rose from about 30% to 60% from 1998 to 2003.

Internet growth brought new listening options to the market, like MP3 players and Internet radio.

Satellite radio followed soon after the introduction of the Internet. Due to fear of broadcast

radio’s ability to maintain profitability in the face of this new competition, Congress first raised

radio ownership caps with the 1996 Telecommunications Act. This led to a wave of stationmergers and acquisitions (M&As). In 2002, the Federal Communications Commission (FCC)

reaffirmed the new ownership caps and also raised caps for television. The effects of this

widespread consolidation on radio audiences are still not fully understood.

This article considers the causes of declining radio listening and explores its implications

for programming content, audience composition, and public policy. I examine a panel ofdata describing radio listening, market structure, demographic trends, and Internet penetration

Correspondence should be addressed to Catherine Tyler Mooney, Department of Economics, University of

Oklahoma, 729 Elm Ave., Hester Hall 329, Norman, OK 73019. E-mail: ctmooney@ou.edu

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across U.S. cities from 1998 to 2003. First, I estimate the effects of various changes in

demographics, technological factors, and the radio industry on the trend in total market radiolistening. Cross-sectional variation in radio ownership caps provides an instrument for changes

in market structure in both linear regression and two-stage least squares (2SLS) estimation. I

find little empirical evidence that the growth of the Internet provided significant competition

to radio stations during this time period. Instead, the results show that changes in radio station

ownership, which have been shown to increase “business-stealing” and advertising, led todecreases in local audience sizes.

I also estimate a discrete-choice model of audience demand for various types of programming

to determine how demand for programming has shifted with declining listening. Not only have

changes in the industry reduced audience size, they also altered the audience distribution across

programming formats. Listening to formats with older audiences and some minority-targeted

formats grew with new technology. These consumers tend to adopt new technology at lowerrates than younger White consumers. Audiences for country music stations grew with increased

ownership caps, perhaps due to economies of scale in programming.

The following section describes changes in the radio industry during the late 1990s and

early 2000s, and presents some results from previous research. The second section describes

the data. The third section describes the empirical methods and findings, and the final sectionconcludes.

BACKGROUND

During the period from 1995 through 2008, Congress and the FCC relaxed long-standing

restrictions on the number of broadcast television and radio stations a single entity could own.They cited competition from new technology and new sources of news and information as

the primary justification for two reasons: (a) The new media outlets provided new voices and,

thus, new content, reducing the need to protect the public from potential monopolization of

the media; and (b) increased competition implied a need to allow the owners of traditional

media to consolidate in order to maintain profitability. In its 2002 Biennial Review of Media

Ownership Rules, the FCC describes its rationale:

While the march of technology has brought to our homes, schools, and places of employment

unprecedented access to information and programming, our broadcast ownership rules, like a

distant echo from the past, continue to restrict who may hold radio and television licenses as

if broadcasters were America’s information gatekeepers: : : : Neither from a policy perspective nor

a legal perspective can rules premised on such a flawed foundation be defended as necessary in

the public interest.1

The National Radio Ownership Rule, which had capped the number of stations a single firm

could own at 40, was eliminated with the 1996 Telecommunications Act. The Local Radio

Ownership Rule now varies the ownership cap by the number of stations in the local radiomarket, and nearly doubled the cap from its previous level in most cities. The policy change was

followed by a wave of consolidation in the industry, with M&A activity peaking around 1999.

1U.S. Federal Communications Commission (2003), p. 4.

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Previous research studying the effects of radio ownership caps have found increases in variety

(see Berry & Waldfogel, 2001; Chipty, 2007; Sweeting, 2006;), decreases in local content duringcertain times of the day (see Chipty, 2007), and increases in advertising in some markets (see

Mooney, 2009), with consolidation. The previous research, especially Romeo and Dick (2003),

found increases in individual stations’ ratings with a merger. They also showed that cost savings

are an important, perhaps more important, motivation behind mergers. Sweeting showed that

these increases in merged stations’ ratings generally come at the expense of other competingstations in the market. Thus, the net effect of station mergers on total market listening could be

negative. Of course, the firm’s objective is to maximize advertising revenue, not listenership.

Mooney took this incentive into account, showing that local station mergers usually increase the

incentive to advertise, which has a negative impact on listening. This article adds to the previous

literature by examining the potential impact of the ownership policy change on overall listening

to commercial radio. Consolidation in the radio industry slightly predated the expansion of theInternet and the introduction of satellite radio. Thus, the effects of the policy change may be

confused with those of technology. This article seeks to untangle the impacts of each on radio

listening.

Changes in audience size and composition due to new technology have been established for

other traditional media. George (2008) documented a downward trend in newspaper readershipcoinciding with a narrowing gap between minority and White readership and growth of

the Internet. She showed that the Internet disproportionately attracts an educated, White,

urban audience away from newspapers. As a consequence, newspapers have shifted their

content toward minorities, education, and crime, which may be of greater interest to their

demographically altered base of subscribers. I also describe a downward trend in broadcastradio listenership that occurred during the Internet growth phase. This coincided with growth

in “urban radio” and, to a lesser extent, Spanish language listening. These radio formats are

both strongly targeted toward minority audiences. Could competition from music on the Internet

have disproportionately attracted White listeners away from broadcast radio, leading stations

to shift their programming toward an increasingly minority audience? The results presented

here show that this is not the case for the Internet. However, satellite radio may have a smalleffect of this type for African American listeners.

New music technology, like the iPod®, differentially attracts younger, higher income con-

sumers, whereas satellite radio attracts an older, affluent audience. For both, the availability

of new technology only affects radio audience size when it coincides with significantly higher

incomes. Unlike reading the news, enjoying audio content online is greatly enhanced by priceyhardware. This implies that, over time, broadcast radio may lose high-income consumers.

Young people have adopted new music technology, especially MP3 players, at a much higher

rate than older consumers, whereas middle-aged consumers have adopted satellite radio at a

higher rate. For either age group, new technologies create a national, if not international, market

for music and other programming. Larger markets can support more variety. For listeners withpeculiar, or “minority,” tastes, the Internet and satellite radio hold more value, as they offer an

alternative market where they may purchase music that more closely matches their preferences.2

Thus, the age composition of radio audiences is likely changing, but the direction of change is

2Waldfogel (2003) described the relation between “minority” tastes and market size as it applies to the radio

industry.

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not obvious, and the future direction will depend on the growth of competing new technologies.

The empirical results suggest that the audiences of “oldies” and religious stations, which tendto be slightly older, grow with the introduction of new technology.

DATA

Trend in Radio Listening

Figures 1 and 2 show the levels and annual changes in the percentage of the population listening

to radio in the average market from 1993 to 2003 using data from Duncan (2004). Radiolistening declined throughout the decade. However, the downward trend becomes steeper at

two points. First, in 1998, annual changes in listening fell below �1.5% per year and remained

there. After a brief slow-down in the trend in 2001, listening fell by more than 2.5% per year

in 2002 and 2003. This research focuses on explaining the changes that occurred after 1998.

Summary Statistics

The data are a panel of U.S. metropolitan area radio markets observed annually from 1998

to 2003. They include all metropolitan areas in the United States for which Arbitron®, Inc.

collected radio listening data. During 1998, about 19% of the average metropolitan area’s

FIGURE 1 Average market radio listening.

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FIGURE 2 Annual change in radio listening, averaged across markets. Note. Source: Duncan (2004).

population listened to radio during the morning drive-time. That figure fell below 17% by

2003.

Table 1 presents the listening figures, along with other trends that may explain declining

commercial radio listening. The most drastic changes are the increases in Internet penetrationrates and satellite radio subscriptions. However, other factors include substantial trends within

the radio industry itself. The share of stations with locally based owners fell substantially

after the repeal of the cap on national radio ownership, and the number of nationally co-

owned “sister” stations grew. The number of owners per market fell by 15%, which implies an

increase from 2.0 to 2.5 stations per owner. Non-commercial and publicly funded radio rosein popularity in some markets, but there is no clear increase in its share of listening for the

average market.

Policy change instigated the ownership trends, but local radio market conditions also play

a role. The potential profitability of a merger is higher in markets with higher listening rates,

ceteris paribus. Therefore, I use the policy change and economic conditions as instruments formarket structure. The primary instrument is the market FM radio ownership cap. The Local

Radio Ownership Rule varies the cap on the number of stations a single firm can own in a

market with the total number of stations in the market.3 Where the local cap falls relative to

3The revised Local Radio Ownership Rule allows a single entity to own only five stations in markets with less than

15 stations, six in markets of 15 to 29, seven in markets of 30 to 45, and six in larger markets. This is approximately

twice the prior cap for most markets.

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TABLE 1

Annual Means of Key Variables for Radio Markets

Variable 1998 1999 2000 2001 2002 2003

Listening to radioa (%) 19.0 18.6 17.5 17.2 17.4 16.7

Radio stations 23.9 23.4 23.5 23.5 25.1 25.0

Stations locally owned (%) 50.7 46.0 41.4 36.7 35.1 34.8

Local station owners 11.8 11.5 10.6 10.1 10.1 10.0

National sister stations per local station 96.1 170.7 289.8 384.8 436.8 434.8

Non-commercial listeninga (%) 4.4 4.4 4.5 4.7 4.0 4.0

Mean income $51,577 $55,084 $56,085 $58,800 $59,959 $60,981

Mean age 35.76 35.80 35.82 36.09 36.27 36.49

Internet penetration rate (%) 33.8 50.7 56.3 60.8

Satellite radio subscribersb 0 0 0 27,733 377,106 1,561,060

Format listening sharesa

Rock 16.7% 17.4% 17.8% 17.3% 17.1% 17.5%

Talk 14.8% 14.2% 14.3% 15.7% 15.4% 15.5%

Adult contemporary 11.3% 11.6% 11.5% 12.7% 11.7% 11.3%

Country 11.2% 11.4% 11.5% 12.7% 11.5% 11.2%

Contemporary hits 11.2% 11.7% 11.5% 12.7% 11.9% 11.4%

Spanish 9.7% 10.0% 8.8% 10.1% 9.9% 9.9%

Oldies 6.9% 6.6% 6.8% 6.7% 9.1% 8.5%

Urban 6.2% 6.3% 6.5% 7.0% 10.8% 10.7%

Other 6.0% 6.0% 5.7% 5.3% 5.2% 5.0%

Religious 4.3% 4.4% 4.4% 4.3% 4.0% 4.3%

Note. Data sources: M Street Publications, Arbitron® Inc., and Current Population Survey.aListening figures are average quarterly hour Arbitron ratings. bNational subscribers.

the number of stations in the market is arbitrary due to the nature of the policy rule. Therefore,it serves as an exogenous measure of local market power.

During this time period, the FCC enforced the cap on a case-by-case basis. The relevant

market for a merger was defined by the overlapping broadcast contours of the merging stations.

This “market” would often be similar to the local metropolitan area and the Arbitron-defined

radio market.4 However, depending on the location of relevant antennae within the city, the

market definition could easily change. As a result, in two Arbitron markets with the same num-ber of stations, the FCC could have allowed a different number of owners. This inconsistency

in the Local Radio Ownership Rule generates additional exogenous cross-sectional variation

in the impact of the cap change. Because I cannot replicate the case-by-case application of

the caps, I use the number of stations included in each market by Arbitron to determine the

appropriate cap.Consolidation across markets that followed the elimination of the national radio ownership

cap is captured by the average total number of sister stations belonging to any firm in the

market divided by the total number of stations in the market. This figure rose from an average

of 96 stations in 1998 to 435 in 2003. At the same time, the percentage of stations with a local

4With the 2003 Review of Media Ownership Regulations, the Local Radio Ownership Rule was modified so that

the rule is enforced using the Arbitron-defined radio market.

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owner fell from an average across all markets of 50 in 1998 to only 35 in 2003. It is likely

that local ownership of radio stations is endogenous in a model explaining market listening,but the number of stations owned by local firms in other markets is determined at a national

level and based on the characteristics of other markets. Thus, I use the latter as an explanatory

variable in the regression analysis.

Public radio also grew in popularity during this time period. Markets with popular public

radio stations are designated whenever the average quarterly hour (AQH) audience of non-commercial radio is larger than 5% of the market population.

Both of the two major technological competitors to radio, the Internet and satellite radio,

were introduced after the ongoing decline in listening had begun. The average market Internet

penetration rate rose dramatically from 1998 to 2000 when it increased by 50%. The first

satellite radio service was not launched until late 2001. Radio listening had already dropped

from 19% to 17% by then. However, competition for some listeners had certainly increaseddrastically by 2003 when XM® and Sirius® satellite broadcasters had a combined national

subscriber base of 1 1/2 million.

The lower panel of Table 1 presents trends in the share of all listening to stations of

a particular programming format and market, averaged across markets. Slight increases in

listening occurred for the two most popular formats, “rock” and “talk.” “Oldies” and especially“urban” programming saw larger upward trends. Listening to the “other” category, which

includes classical and jazz stations, decreased by about one percentage point. Audience share

for the rest of the formats remained fairly constant. The trends in urban, contemporary hits,

oldies, and Spanish stations are particularly interesting because these formats are strongly

associated with certain demographic groups.

Data Sources

Arbitron, the primary industry source for radio ratings, pays radio listeners to keep diaries of

their listening habits during two 6-week periods each year. For each quarter hour of the day,

Arbitron counts the number of respondents who listen to a particular station for at least 5 min.If a respondent listens for 10 complete minutes, they are recorded twice, and likewise for 15

min. The share of respondents for each station during the AQH is the station’s rating for a

particular time of day. The day is divided into four parts: morning (6 a.m.–10 a.m.), day (10

a.m.–2 p.m.), p.m. (3 p.m.–7 p.m.), and evening (7 p.m.–12 a.m.). The total market rating for

each day-part is the sum of the AQH ratings over all stations.The other radio station data were published by M Street Publications. They include station

identifiers, ownership, and programming format. The number of stations in the data grows over

time. This is primarily due to an increase in the number of stations rated by Arbitron, but it

is also partially due to a small increase in FCC station licenses. I do not include stations with

so few listeners that they are not rated.I merge the radio data with select variables from the Current Population Survey (CPS).

For every year, the CPS includes metropolitan area demographic information. During 1998,

2000, 2001, and 2003, it also included a special supplement on computer usage. The supplement

included Internet usage at work, at home, and in public for each respondent. Because consumers

are most likely to download music for entertainment at home, I compute at-home Internet

penetration rates by metropolitan area market. The other new technological competitor to

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broadcast radio is satellite radio. Although satellite radio subscriber data is not readily available

for metropolitan areas, national figures are reported annually in the companies SEC 10K filings.

EMPIRICAL STRATEGY AND RESULTS

It is difficult to untangle popular trends in consumer preferences from the introduction of

substitute products provided by new technology and other changes in the radio industry.

Therefore, the empirical strategy relies on exploiting cross-sectional variation to explain changes

in broadcast radio listening patterns. For the total listening analysis, I use levels and changesin the explanatory variables in each market to explain the difference in total market listening

between years. For the discrete-choice model, I use programming format/year fixed effects

to control for national trends in music preferences. I can then identify the effects of new

technology and radio market structure using variation in demographics, Internet penetration,

and ownership caps across markets.

Total Listening Analysis

The goal of the total market listening analysis is to understand how changes in the radio

industry, as well as in the availability and affordability of new technology, affect listening. Iuse two different approaches to address the question at hand. First, I measure the effects of

the local radio ownership cap, along with other measures of market structure, demographics,

and competition from new technology, on radio listening using a linear regression approach.

The results show that a higher cap leads to a smaller local radio audience. The second sub-

section considers the impact of the caps on local market concentration, and then estimates the

corresponding effect of local ownership on listening using 2SLS.

The effect of local ownership caps on listening. I regress the change in the log of the

listening proportion of the population on changes and levels of several explanatory variablesthat capture changes in competition and market structure for commercial broadcast radio. These

include the Internet penetration rate, mean income, mean age, the local radio ownership cap,

the average national firm size represented in the market (as measured by the number of stations

owned), and a dummy variable for popular public radio stations. I also control for market size

with the number of stations in the market. The data include four observations for each market(m) representing the different times of the day (j ). Dummy variables capture the differences

across day-parts. The model is as follows:

� log.listeningj m/ D ˛0 C ˛1�Internetm C ˛2�incomem C ˛3�agem C ˛4�stationsm

C ˛5�sisterstationsm C ˛6�publicm C ˛7Internetm C ˛8incomem C ˛9agem

C ˛101m.Cap D 6=4/ C ˛111m.Cap D 7=4/ C ˛121m.Cap D 8=6/ C ˛13sisterstationsm

C ˛14publicm C ˛15stationsm C Dj C �jm;

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where Dj represents the dummy variables for the four day-parts. The local radio ownership

cap is captured using dummy variables for each cap level. The first number is the total cap,and the second is the cap on stations in the same service (AM or FM). The 7th through 15th

variables, the levels, are measured in the first year of each pair, allowing the change in listening

to vary with initial market conditions.

A benefit of the differenced regression approach is that any time-invariant unobserved market

effects in �j m difference out of the model. However, unobserved time trends do not. AppendixB presents a panel fixed-effects version of the model, allowing for autocorrelation. The results

are robust to the change of specification. The model also allows the variance of the error to

differ by day-part.

Identification of the impact of mergers on radio listening relies on the exogenous variation

in consolidation induced by the multiple levels of ownership caps, combined with the varying

pace of M&A activity during the time period. Therefore, I estimate this model for 1998 though2003, 1998 through 2000, and 2000 through 2003, breaking up the time period at the peak

of radio merger activity.5 Although this is not a pure differences-in-differences approach, it

reveals the impact of the policy change up to and after the peak of consolidation. Significantly

different parameter estimates before and after 2000, presented in Table 2, indicate a distinct

change in the listening trend associated with the ownership caps. Listening was falling duringthe entire period for the markets with the highest cap, indicating a downward trend in listening

for large markets, but the pace of decline associated with the cap doubled from �0.09 to �0.2

after 2000. Radio audiences did not begin to decline for markets with caps of five, six, and

seven until after 2000.

The magnitude of the parameter estimates shows that the local ownership cap played a largerrole in decreased listening than any competitor to commercial radio over the entire period. Even

so, the largest effect estimated is a 0.2% decrease in listening from 2000 to 2003 in markets with

the highest cap. The marginal effect of Internet penetration is even smaller, about �0.03, at the

mean across all markets of 0.33 in 1998, and it declines over time, becoming positive in 2000.

The consistent, negative effect of income across all time periods shows that listening decreases

more for consumers with more purchasing power. At the mean income of $52,000 in 1998,the marginal effect of a $10,000 increase in income is �0.07—about the same magnitude

as the impact of the local ownership caps. This result is not surprising given that the new

hardware necessary to enjoy online music and satellite radio was fairly expensive. The results

are inconclusive regarding the influence of mean age, but its effects are not particularly large.

These results contrast those found by George (2008) for the newspaper industry. She foundthat the Internet played a large role in declining newspaper subscriptions. Although both

newspapers and radio are traditional media, the means of consumption are quite different.

Thus, there is no necessary reason that competition from the Internet should affect broadcast

radio usage in the same way. Most radio listening takes place while the listener is pursuing

another activity, like working or driving a car. Reading the news, on the other hand, requiresa captive consumer. Also, reading news on the Internet is not largely affected by the speed of

the Internet connection, as is music downloading or live streaming Internet radio, and it is not

enhanced by additional purchases like compact disc burners or MP3 players.

5Appendix A includes similar results for 1998 through 2001 and 2001 through 2003.

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TABLE 2

Changes in Market Percentage Listening to Radio With Ownership Caps (Ordinary Least Squares Results)

1998–2003 1998–2000 2000–2003

Variable

Parameter

Estimate SE

Parameter

Estimate SE

Parameter

Estimate SE

Intercept �0.10 0.06 �0.06 0.06 �0.15 0.03***

� Internet penetration 0.05 0.06 �0.10 0.02*** 0.19 0.04***

� Mean income �6.03 6.03 �0.01 5.03 �2.03 2.03

� Mean age 8.04 8.04 3.03 2.03 4.03 9.04***

� Commercial stations 8.03 1.03*** 0.01 2.03*** 0.01 3.04***

� Mean national owned stations 2.06 2.06 2.05 5.06*** �1.05 4.06*

� High public radio share �0.09 0.02*** �9.04 0.01 �0.09 0.03***

Internet penetration �0.08 0.02*** �0.05 0.01*** 0.18 0.03***

Mean income ($10,000) �0.01 4.04*** 0.00 3.03 �0.02 3.03***

Mean age 4.05 6.04 1.04 1.03 1.03 4.04**

Ownership cap 6/4 �0.06 0.02** �0.01 0.01 �0.09 0.03**

Ownership cap 7/4 �0.09 0.02*** �1.03 0.01 �0.18 0.03***

Ownership cap 8/5 �0.14 0.04*** �0.09 0.04** �0.20 0.03***

Mean national owned stations �9.06 1.05 �3.05 1.05** �6.06 6.06

High public radio share �5.03 0.01 0.03 0.00*** �0.06 0.03**

Commercial stations 4.03 7.04*** 1.03 5.04** 6.03 3.04***

R2 0.15 0.08 0.12

Observations 917 909 936

Note. Data sources: M Street Publications, Arbitron® Inc., and Current Population Survey.

*p D :15. **p D :10. ***p D :05.

The previous economic literature provides potential explanations for the decrease in listeningwith higher radio ownership caps. Consumers’ and musicians’ advocates have long criticized

the deregulation of radio ownership for decreasing access to radio for new music and for

decreasing variety. However, previous research has shown that variety did not fall during this

time period.6 Aspects of consolidation, other than the chosen music, must drive the negative

effect on listening. As described earlier, Mooney (2009) showed that radio consolidation often

leads to increased advertising, which audiences tend to dislike, but the increases are tempered bylisteners’ tendency to switch off stations with too many ads. Romeo and Dick (2003) showed

that format changes with mergers often lead to increases in station ratings for the acquired

stations. However, Sweeting (2006) found that the increase in ratings for the merging stations

often comes at the expense of other stations in the market, which could lead to the observed

decrease in total listening.During its 2006 review of media ownership policy, the FCC placed focus on the localism

of programming, which generally declines with national ownership. The results here are

inconclusive regarding listeners’ tastes for the programming of large, national station owners.

The estimate on the change in national owners is positive from 1998 to 2000 and negative

6See Sweeting (2006) and Berry and Waldfogel (2001).

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from 2000 to 2003. The net effect is a wash. The mean level of national sister stations always

negatively impacts listening, but the parameter estimate is quite small.In the 4% of markets where public radio experienced a substantial increase in listening,

commercial stations lost less than one tenth of a point of market share. The impact is not large.

In fact, Berry and Waldfogel (1999) found that only the rare classical and jazz commercial

stations compete directly with public radio.

Local ownership and radio listening. Local radio ownership policy impacts radio lis-

tening because merged stations change their strategic behavior. Stations make both subtle

and overt changes to their programming content and the amount of broadcast time devoted to

advertising in response to their competitive environment. These strategies affect radio listening.

Precise description of all these changes is difficult without a structural model, but the number ofowners in a market relative to the number of stations can capture much of the effects in a simple

linear model. Of course, stations with larger potential audiences find mergers more profitable.

Therefore, the number of owners is endogenous. I use the ownership caps as instruments for

the number of owners in each market.

This approach is similar to that used by Berry and Waldfogel (2001). They found no impact

of the change in ownership caps on local audience sizes. However, they only considered changesin the radio industry up to 1997. The results here show that, although M&As took place in re-

sponse to the policy change as early as 1997, many of the consequences of the policy change did

not come to fruition until the early 2000s when M&A activity reached its peak. Perhaps firms

learned how to benefit from strategies like adjusting advertising or changing their programming

to enhance their competitive position over time, as they grew. The larger national presenceachieved by many firms after 2000 allowed them to share successful strategies across radio

markets. This is particularly clear in the 1998 through 2001 results presented in Appendix A.

Table 3 presents the estimates of the effects of the ownership caps, along with the other

market characteristics on the levels and changes in the number of station owners in each

market. They show that the caps explain a great deal of the variation in the number of ownersper market in 1998, but less of the variation in the change in ownership from 1998 to 2003. The

number of owners falls, relative to the number of stations, with income and age, in addition

to the local ownership cap. Because the ownership caps are not as good an instrument for the

change in owners, I use only the number of owners in the second-stage regression.

The 2SLS model is as follows:

� log.listeningj m/ D ˛0 C ˛1�Internetm C ˛2�incomem C ˛3�agem C ˛4�stationsm

C ˛5�sisterstationsm C ˛6�publicm C ˛7Internetm C ˛8incomem C ˛9agem

C ˛10OOm C ˛11sisterstationsm C ˛12publicm C ˛13stationsm C Dj C �jm;

where OOm D XmO, OOm is the number of owners in the market, and Xm includes the market

characteristics and the dummy variables for the ownership cap. The parameter estimates

presented in the second column of Table 3 form O.

Table 4 presents the results along with ordinary least squares estimates. The parameter

estimates on the number of owners reveals the expected bias. If, for a given market size,

more M&As occur in markets with larger audiences, listening rates are higher in markets with

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TABLE 3

First-Stage Regressions Explaining Local Ownership, 1998–2003

� Local Owners Local Owners

Variable

Parameter

Estimate SE

Parameter

Estimate SE

Intercept 3.28 0.10*** 4.80 0.11***

Ownership cap 6/4 0.35 0.03*** �0.73 0.02***

Ownership cap 7/4 0.53 0.04*** �1.75 0.02***

Ownership cap 8/5 1.08 0.08*** �4.04 0.03***

� Internet penetration �1.59 0.28*** �3.01 0.58***

� Mean income �0.11 5.03*** 0.09 0.01***

� Mean age �0.01 2.03*** �0.06 0.00***

� Commercial stations 0.38 2.03*** 0.04 0.00***

� High public radio share �0.43 0.21* 0.55 0.07***

Internet penetration �21.02 0.67*** 2.64 0.55***

Mean income �0.02 3.03*** �0.28 0.01***

Mean age �0.08 0.00*** �0.02 0.01*

High public radio share �0.63 0.16*** 0.56 0.20**

Commercial stations �0.06 2.03*** 0.50 1.03***

R2 0.31 0.71

Observations 917 917

Note. Data sources: M Street Publications, Arbitron® Inc., and Current Population

Survey. Regressions include fixed effects and clustered standard errors to account for

differences across day-parts.

*p D :15. **p D :10. ***p D :05.

more owners. Thus, the parameter is biased downward. The two-equation model describes how

market size, income, age, and ownership caps affect the number of owners. If the ownershipcaps are a valid instrument for the number of owners, the positive 2SLS estimate is consistent

and removes the bias.

At an average of 8.6 owners per market, the marginal effect implies that one more owner in

a market increases listening by about 0.24%. This is remarkably larger than the marginal effect

of increasing the ownership cap from, say, seven to eight as measured by the previous model,which is �0.05%. It is interesting to note that these estimates indicate a clear, statistically

significant effect of the Internet penetration rate on listening. However, the magnitude of the

marginal effect is only 0.01%. Here, I do not find a significant role for income in explaining

declines in radio listening. The 2SLS results reaffirm the role of local ownership in the decline

in radio listening and the very small impact of new technology.

Changes in Demand by Format

The goal of the format demand model is to describe variation in listener demand for formats

with Internet penetration rates, growth in satellite radio, and increased ownership caps. Radio

listeners make a discrete choice among the available radio station formats each time they tune

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TABLE 4

Changes in Market Percentage Listening to Radio With Owners for 1998–2003

OLS 2SLS

Variable

Parameter

Estimate SE

Parameter

Estimate SE

Intercept �0.10 0.05 �0.27 0.05***

� Internet penetration 0.03 0.06 0.19 0.05***

� Mean income �4.03 6.03 �0.01 5.03*

� Mean age 1.03 8.04 2.03 9.04***

� Commercial stations 0.01 7.04*** 0.01 1.03***

� Local owners �1.03 2.03

� Mean national owned stations �1.06 3.06 7.06 1.06***

� High public radio share �0.09 0.02*** �0.10 0.02***

Internet penetration �0.10 0.02*** �0.04 0.02***

Mean income �0.01 6.04*** �2.03 1.03

Mean age 4.04 7.04 4.04 7.04

Local owners �3.03 8.04*** 0.03 4.03***

Mean national owned stations �1.05 1.05 1.05 1.05

High public radio share �6.04 0.01 �0.02 0.01*

Commercial stations 3.03 6.04*** �0.01 2.03***

R2 0.14

Observations 917 917

Note. Data sources: M Street Publications, Arbitron® Inc., and Current Population Survey.

Regressions include fixed effects and clustered standard errors to account for differences across

day-parts. OLS D ordinary least squares; 2SLS D two-stage least squares.

*p D :15. **p D :10. ***p D :05.

in. Each listener’s utility from listening to the radio depends on their strength of preference for

the chosen format, which is

uifmt D Xfmtˇ C �fmt C "ifmt;

where Xfmt is the interaction of radio station formats, f , with data describing the changing

technology, ownership, and demographics in Market m during Year t . Unobserved variation inthe quality of the stations available in each format, market, and year are captured by the error,

�fmt, and "ifmt captures listener-specific variation in preferences.

I separate out a fixed format–time period effect, Dft, from �fmt to capture aggregate trends

in tastes and denote market deviations from the trend, Q�fmt. With the assumption that the

listener-specific heterogeneity, "ifmt, is distributed independent and identically distributed Type-1extreme value, Berry (1994) showed that the parameters of the model can be estimated with

the following equation:

ln.sfmt/ � ln.somt/ D Xfmtˇ C Dft C Q�fmt;

where the dependent variable is the difference in the natural log of the format’s market share

and the share of the outside good (not listening to commercial radio).

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The results, presented in Table 5, reveal increases in country music listening with increased

ownership caps. Due to the consistency in country programming from market to market and itslack of overlap with other formats, this format is a good candidate to experience economies of

scale with consolidation. Generally, ownership caps do not appear to have much impact on the

distribution of listening across formats, nor do Internet penetration rates, which lead to only a

slight decrease in country music listening.

TABLE 5

Discrete-Choice Model of Market Listener Demand by Format: 2000, 2001, and 2003

Explanatory Variable

Format

Interaction

Variable

Mean

Parameter

Estimate SE

Intercept �2.418 0.641**Ownership cap 0.272 �0.107 0.098

Talk 0.039 0.098Country 0.177 0.098*

Rock 0.104 0.098Spanish 0.208 0.175

Religious �0.100 0.098Contemporary hits 0.061 0.116Adult contemporary 0.102 0.098

Urban �0.026 0.098Oldies 0.109 0.113

Internet penetration rate 0.507 0.208 0.269Talk 0.358 0.346

Country �0.815 0.335**Rock 0.120 0.305Spanish 0.081 0.615

Religious �0.150 0.378Contemporary hits �0.479 0.371

Adult contemporary 0.276 0.287Urban 0.416 0.584

Oldies �0.021 0.311Satellite Radio � Mean Income 20.557 �1.703 1.403

Talk 1.403 2.303

Country 3.903 3.203Rock �6.904 1.903

Spanish �2.805 4.303Religious 4.503 1.103**

Contemporary hits 2.704 3.003Adult contemporary 1.503 8.404*

Urban 5.003 3.603Oldies 4.803 9.004**

R2 0.62

Observations 7,767

Note. Data sources: M Street Publications, Arbitron® Inc., Current Population Survey, XM®, and Sirius® SECForm 10k. The omitted category is “other,” which includes classical, jazz, and miscellaneous formats. Estimates of

format–year fixed effects and format–demographic interactions are available upon request. Asymptotic standard errorsaccount for heteroskedasticity across markets.

*p D :10. **p D :05.

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Satellite radio subscriber data is only available at the national level. Because it is a costly

service, I interact the annual subscriber figures with average market income to identify thecross-sectional effects of satellite radio on listener preferences. The model also includes controls

for the effect of income alone to isolate the impact of satellite radio. The results show that,

in contrast to the Internet, listening to religious stations increases with satellite radio. This

finding is consistent with lower adoption of new technology by minority groups, leading to

increased listening to traditional broadcast radio—the same kind of effect found by George(2008). Satellite radio also increases the relative audience size for adult contemporary and

oldies stations, which contradicts expectations because satellite radio subscribers are older than

the average broadcast radio listener.

CONCLUSION

Broadcast radio audiences may continue to decline with competition from new technology, but

the evidence found here shows that the trend is not as strong as suspected. I find that the primary

factor driving decreased listening from 1998 to 2003 is the increase in radio ownership caps. In-

come and age effects reveal that new technology also plays a small role, but Internet penetrationrates do not affect total listening as much as one might expect. The discrete-choice model shows

that the distribution of listening over programming formats is impacted more by the Internet,

and by satellite radio, but the effects are not especially large. As policymakers continue to

regulate the radio, satellite radio, and Internet industries, it is important that they understand

the impacts of long-term trends in technology and consumer behavior. This article raises two

key issues in that regard. First, new technology does not have the same impact on all media orall audiences. Second, other important changes in the industry, like M&As, may affect the same

outcomes as new technology, exaggerating its impacts. Moreover, policies aimed to maintain

the viability of commercial broadcast radio, like raising ownership caps, can actually have

adverse effects on listening, and possibly unintended consequences for audience composition.

ACKNOWLEDGMENTS

I thank Zenobia Mehta Sribnick for assistance with the data, A. John Mooney for helpful con-

versations and editing, and Aubrey Gamble for research assistance. Thanks also to participants

in the 2008 Southern Economics Association annual meeting in Washington, DC. All errorsare my own.

REFERENCES

Berry, S. T. (1994). Estimating discrete choice models of product differentiation. RAND Journal of Economics, 25,

242–262.

Berry, S. T., & Waldfogel, J. (1999). Public radio in the United States: Does it correct market failure or cannibalize

commercial stations? Journal of Public Economics, 71, 189–211.

Berry, S. T., & Waldfogel, J. (2001). Do mergers increase product variety? Evidence from radio broadcasting. The

Quarterly Journal of Economics, 116(3), 1009–1025.

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Chipty, T. (2007). Station ownership and programming in radio. Washington, DC: Federal Communications Commis-

sion, Research Studies on Media Ownership.

Duncan, J. (2004). An American Radio Triology: 1975 to 2004, Vol. 1. Tesuque, New Mexico: Duncan’s American

Radio.

George, L. (2008). The Internet and the market for daily newspapers. B.E. Journal of Economic Analysis and Policy,

8(1), Article 26.

Mooney, C. T. (2009). A two-sided market analysis of radio ownership caps (Working Paper No.). Norman: University

of Oklahoma.

Romeo, C. J., & Dick, A. (2003). The effect of format changes and ownership consolidation on radio station outcomes.

Review of Industrial Organization, 27, 351–386.

Sweeting, A. (2006). Too much rock and roll? Station ownership, programming and listenership in the music radio

industry (Working Paper No.). Durham, NC: Duke University, Department of Economics.

Telecommunications Act of 1996. (1996). United States Cong. Senate. 104th Cong., 2nd sess. S 652. Washington:

GPO.

U.S. Federal Communications Commission. (2003). 2002 Biennial Regulatory Review—Review of the Commission’s

Broadcast Ownership Rules and Other Rules Adopted Pursuant to Section 202 of the Telecommunications Act of

1996, Report & Order & Notice of Proposed Rulemaking, FCC MB Docket 02-277, adopted June 2, 2003.

Waldfogel, J. (2003). Preference externalities: An empirical study of who benefits whom in differentiated-product

markets. RAND Journal of Economics, 34, 557–568.

APPENDIX A

TABLE A1

Changes in Market Percentage Listening to Radio With Ownership Caps

(Ordinary Least Squares Results)

1998–2001 2001–2003

Variable

Parameter

Estimate SE

Parameter

Estimate SE

Intercept �0.22 0.06*** 0.05 0.03� Internet penetration �0.09 0.02*** �0.06 0.04� Mean income 0.01 5.03 �4.03 5.03� Mean age 3.03 2.03 �3.03 1.03**� Commercial stations 0.01 2.03*** 8.03 1.03***� Mean national owned stations 1.05 3.06*** 3.05 7.06***� High public radio share �0.07 8.03*** �0.10 0.02***Internet penetration �0.10 0.05 0.09 0.03**Mean income 6.04 1.03 �0.01 1.03***Mean age 3.03 1.03* �2.03 8.04**Ownership cap 6/4 �5.03 0.02 �0.07 0.03*Ownership cap 7/4 �0.08 0.04* �0.08 0.04Ownership cap 8/5 �0.14 0.05** �0.11 0.06*Mean national owned stations �4.05 1.05*** �9.06 4.06*High public radio share �0.01 4.03** �7.03 0.02Commercial stations 5.03 1.03*** 3.03 1.03***

R2 0.19 0.18Observations 913 940

Note. Data sources: M Street Publications, Arbitron® Inc., and Current Population Survey.Regressions include fixed effects and clustered standard errors to account for differences acrossday-parts.

*p D :15. **p D :10. ***p D :05.

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APPENDIX B

PANEL DATA MODEL WITH AUTOCORRELATIONAND FIXED EFFECTS

In each time period, radio listening depends on the characteristics of the radio stations, market

structures, demographics, and competing technologies. The model is

lmt D Xmtˇ C �m C "mt;

where the vector Xmt represents the market and station characteristics in t , �m represents time-

invariant market characteristics, and "mt captures unobserved market–time effects. If the errors

in the model are correlated over time according to a basic autoregressive process with one lag,the last term becomes

"mt D �"mt�1 C umt:

The first-differenced regression is

�lmt D �Xmtˇ C �"mt;

TABLE B1Panel Data Estimation of Market Percentage Listening

to Radio With Ownership Caps

Variable

Parameter

Estimate SE

Intercept �2.27 0.05*

Mean income 2.03 4.03

Lag of mean income �3.03 4.03

Mean age �5.04 1.03

Lag of mean age �5.04 1.03

Ownership cap 6/4 �0.04 0.01*

Ownership cap 7/4 �0.07 0.02*

Ownership cap 8/5 �0.12 0.03*

Mean national owned stations 9.06 6.06

Lag of mean national owned stations �9.06 4.06*

High public radio share �0.04 0.01*

Lag of high public share 3.03 5.03

Commercial stations 0.01 1.03*

Lag of commercial stations �6.04 1.03

� (AR-1) 0.24

R2 0.004

Observations 3,700

Note. Data sources: M Street Publications, Arbitron® Inc.,

and Current Population Survey. Regression includes market/day-part

fixed effects and AR-1 autocorrelated errors.

*p D :05.

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where �lmt D lmt � lmt�1, and likewise for X and ". Note that the unobserved market effects,

�m, difference out of the equation. The other error does not: �"mt D "mt � "mt�1 D .� �

1/"mt�1 C umt. Note that if � D 0, �"mt is the sum of two independent and identically

distributed random variables, and ordinary least squares is a consistent and efficient estimator

of ˇ.

I use the first model to test for autocorrelation in the differenced data. I reintroduce the

lags of X as explanatory variables as in the previous model, and I do not include the Internetpenetration rates, which are not available for all years. Thus, the panel consists of Arbitron®

radio markets observed annually from 1998 to 2003. I find that the estimated � is 0.24. In

the differenced model, the differences are over 2 to 5 years. Based on this estimate, the

autocorrelations would be �2 D 0:06 and �5 D 8 � 104, respectively, which are small. The

fixed effects model, with an R2 statistic of 0.004, does not explain the levels of the listening

share very well. However, the conclusions regarding the effects of the explanatory variables,especially the ownership caps, are robust to the panel data specification.

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