FEDERAL COMMUNICATIONS COMMISSION

MEDIA BUREAU STAFF RESEARCH PAPER 

Media Ownership Working Group 

Consolidation and 

Advertising Prices in 

Local Radio Markets 

By Keith Brown and George Williams 

September 2002

2002 - 

4 
1

Consolidation and Advertising Price in Local Radio Markets 

Keith Brown and George Williams*1

Executive Summary: We combine data on the price charged to national and regional 

advertising agencies for local radio advertising with data on concentration in local radio 

markets, creating a panel data set relating the price of advertising to concentration. We 

employ fixed-effects regression and find that changes in local concentration from 1996-

2001 explain 3-4% out of the 68% increase in real advertising rates during this period. 

Economic growth explains much of the other 65%. National concentration does not 

appear to drive the increase in advertising prices. A greater presence of large national 

owners in a local market appears to decrease the advertising rates paid by national and 

regional advertising agencies. 

Acknowledgements: We would like to thank all the participants at the Media Bureau 

Brown Bag lunch for helpful comments, and we’re especially grateful to Ken Lynch for 

his valuable and helpful expertise. All errors are our own. 

 

*

 Following convention, we list authors in alphabetical order of surname. 

1

 Media Bureau, Federal Communications Commission. The views expressed in this paper are those of the 

authors alone and do not necessarily reflect the views of the Federal Communications Commission, any 

Commissioners, or other staff. 
2

I. Introduction 

 With the wave of radio mergers following the 1996 Telecommunications Act, the 

radio market has captured more attention from observers and policymakers. Specifically, 

observers and policymakers wish to understand the causes and consequences of 

consolidation in radio markets. Combining data on the price of local radio advertising2

with data on radio market characteristics and economic growth from BIA and the Bureau 

of Economic Analysis, we analyze the effect of consolidation on the price of radio 

advertising. We find that increases in local concentration following the Act modestly 

increased local radio advertising prices, while increases in national concentration had no 

apparent effect. When we examined markets where the local share of national 

conglomerate owners grew more quickly over time, we found that these markets actually 

charged lower prices for local radio advertising to national and regional advertising 

agencies. 

 The 1996 Act made two major regulatory changes in radio. First, the 1996 Act 

increased the number of radio stations that a single radio owner could possess in any 

given locality. Under the 1996 Act, a single owner can own up to 8 radio stations in a 

market with 45 or more commercial radio stations, 7 radio stations in a market with 30-44 

 

2

 Provided by Service Quality Analytics Data (SQAD). Because our radio advertising price data derives 

exclusively from the records of national and regional advertising agencies, it only includes the local radio 

advertising purchases made through these advertising agencies, and does not include data on local radio 

advertising purchases made by actual local businesses. 
3

radio stations, 6 radio stations in a market with 15-29 radio stations, and five stations in 

markets with less than 15 radio stations. Second, the 1996 Act eliminated all caps on 

national ownership, replacing the old cap of 20 FM stations and 20 AM stations. This 

paper specifically addresses and estimates the effects of these changes on the prices paid 

by national and regional advertising agencies for advertising time in local radio markets. 

 Even if we could fully apprehend the effects of radio consolidation on the prices 

paid by all types of advertisers for all types of radio advertising, the economic welfare 

implications of consolidation in radio markets would still be unclear, because advertisers 

are not the only customers in radio markets. Listeners also consume radio, and listeners 

may not like advertising. To the extent that listeners dislike advertising, and to the extent 

radio stations use market power to charge advertisers higher prices by restricting the 

amount of advertising time, then listeners may in fact benefit from the radio stations’ 

exercise of market power over advertisers.3

 

II. Literature Review

 Consolidation in media markets effects economic welfare in many disparate ways. 

Berry and Waldfogel (2001) studied the effects of consolidation on format diversity in 

 

3 Becker and Murphy (1993) infer that media consumers in any advertiser-supported media market dislike 

the advertising carried by that media from the fact that the advertiser must compensate the media consumer 

with free programming in order to get the media consumer to view or listen to the advertising. Therefore, 

under that theory, if market power by radio owners leads to higher advertising prices paid by radio 

advertisers, then radio listeners could in fact benefit from the resulting decline in the quantity of 

advertising. 
4

radio markets, finding that consolidation increased the number of formats available to 

listeners. Romeo and Dick (2001) contend that a radio station’s chosen format represents 

a significant sunk cost, indicating that concentration within a local radio market’s format 

may be meaningful for antitrust analysis. 

 Radio consolidation may also impact the advertising market. The Department of 

Justice has expressed specific interest in understanding the effects of recent radio mergers 

on advertising rates. From a theoretical perspective, the impact of consolidation on radio 

markets allows us to explore the implications of recent theory. Anderson and Coate 

(2000) contend that firms in advertiser-supported media markets have two potential 

sources of market power; market power over listeners/viewers and market power over 

advertisers. In fact, Anderson and Coate point out, these two different sources of market 

power counteract each other when determining the amount of advertising in the market. 

To the extent a media firm has market power over listeners/viewers (and if 

listeners/viewers dislike advertising), then the firm will air more ads, because 

listeners/viewers do not have close media substitutes and therefore cannot avoid 

advertising. On the other hand, to the extent a media firm has market power over 

advertisers, then the firm will air less advertising in order to extract higher prices from 

advertisers. 

 Past empirical work on radio advertising includes Ekelund, Ford, and Jackson 

(1999), who estimate the own-price elasticity of radio advertising in order to determine 

whether radio advertising constitutes a distinct local market for antitrust analysis. EFJ 
5

find that radio advertising does indeed constitute an antitrust market. Ekelund, Ford, and 

Koutsky (2000) estimate the sale price of radio stations as a function of concentration. If 

local concentration raises market power for radio stations, then it should raise the 

expected profits of each radio station, which should raise the equilibrium sale price of 

each radio station. EFK find no strong relationship between the sale price of radio 

stations and local concentration. 

 In other work on media advertising substitutability, Silk, Klein, and Berndt (2002) 

employ simultaneous equations to analyze the cross-price elasticity for national 

advertising on eight different types of media. SKB mainly find that national advertising 

on different media are weak substitutes for each other. Seldon, Jewell, and O’Brien 

(2000) analyze the cross-substitutability of print media, television, and radio for beer 

advertisers, finding a very high degree of substitutability. McCullogh and Waldon (1998) 

estimate the substitutability between national spot and network television advertising, 

finding weak substitutability between the two. 

III. Data and Methodology 

Thus far, no researcher has, to our knowledge, yet performed a solid and 

comprehensive panel estimation of the relationship between consolidation and the local 

radio advertising price following the 1996 Act. In addition, no researcher has, to our 

knowledge, yet examined the direct effect of national consolidation on local radio 

advertising price. We fill this gap with a panel data set which tracks real-dollar 
6

advertising rates, the number of stations, local concentration measures (including the 

local Herfindahl Index and the number of owners), population, real income, and national 

concentration for 214 different markets from the 1st Quarter of 1996 (when Congress 

passed the Telecommunications Act) to the 1st Quarter of 2001. This panel allows us to 

track consolidation’s effect on advertising rates. 

 We create our data set by combining Service Quality Analytics Data (SQAD) data 

with BIA data and data from the Bureau of Economic Analysis. The SQAD data derives 

from participating national and regional advertisers, who report the price of their local 

advertising buys for a given radio market. The prices for each local buy for each local 

market are then averaged together, giving a single advertising price for each radio 

market. One primary virtue of SQAD data is that SQAD advertising rates are actual 

rates paid by advertisers for spots on local radio stations in each market, and are not 

derived from rate cards. We take the SQAD CPMs (cost of reaching 1,000 listeners aged 

18-49) and merge these data with data on local market concentration, national market 

concentration, population, and per capita personal income within each market. This 

generates a panel covering 214 local radio markets over a five year period, from 1995-

2000. We match our 1st Quarter SQAD advertising rates in a given year to the BIA and 

BEA data from the year before, because SQAD rates are determined by contract 

negotiations during previous quarters. For example, contracts negotiated by the final 

quarter of 1995 determine the SQAD advertising rates paid for the 1st Quarter of 1996. 

Therefore, in our specification, the 1st Quarter 1996 SQAD advertising price is a function 
7

of market concentration and demographic variables during 1995. Because our first 

SQAD rates are determined by 1995 pre-Act market conditions, the 1996 Act itself 

provides exogenous variation. 

 We recognize the limits of our data. Our advertising prices are aggregated by 

market and they only reflect the prices reported by national and regional advertising 

agencies for purchases of local radio advertising time. The rates paid by local advertisers 

likely differ from the rates paid by national and regional advertisers. However, the 

SQAD data are, to our knowledge, the only data on radio advertising prices that do not 

rely on rate cards, but instead reflect the actual prices paid for advertising. 

 We employ both two-way market fixed-effects and one-way market fixed-effects. 

Using panel data confers serious benefits in any empirical analysis. Thanks to panel data 

methodology, we can use so-called fixed effects to adjust for many things that cannot be 

observed. Market fixed-effects adjust for unseen idiosyncrasies in a given market (a 

popular DJ whose commercial testimonials are unusually effective, for instance) that 

affects the local radio advertising price. Including time fixed-effects adjusts for any 

idiosyncratic event that happens to all local radio markets simultaneously at a given point 

in time. Because the 1996 Act was exactly such an event, we employ econometric 

specifications that include time fixed-effects in order to evaluate the effect of the Act 

itself, and we employ specifications that do NOT include time fixed-effects in order to 

evaluate the effect the Act’s new regulatory regime had on radio advertising rates. We 

use population-weighted least squares as our regression methodology. 
8

IV. The Variables and Descriptive Statistics 

 Because we want to analyze the effects of both national and local consolidation 

on advertising prices, we include both local and national Herfindahl indices (hereafter 

referred to “HHI”). We derive local HHIs for each market in each year by summing the 

squares of each owner’s revenue market share in a market during a given year. This 

gives us a measure of concentration in the local market. This allows us to partially 

evaluate the effects of local consolidation stemming from the 1996 Act’s changes to the 

local ownership rules. We derive national HHIs by summing the squares of each owner’s 

national revenue market share. This gives the level of concentration across all stations in 

all markets. Including the national HHI allows us to test whether eliminating the national 

ownership cap had any effect on the prices paid by national and regional advertisers for 

local radio advertising. Note that the national HHIs are the same for all markets during 

each year. 

The measures of local and national radio market concentration, as measured by 

HHIs, grew significantly from 1995 to 2000, reflecting the consolidation in the radio 

industry during this period. In addition, we also include the raw number of owners in 

each local market, in order to see whether price collusion became easier to coordinate as 

local markets become consolidated among fewer owners. Including the number of 

owners with the local HHI may allow us to distinguish the market-power effects of local 

consolidation (local HHI) from the collusive effects of local consolidation (number of 
9

owners in a local market). We also create a new variable, called local-national, which 

reflects the degree to which a local market contains large national radio firms. We create 

this variable by multiplying each owner’s local market share by that owner’s national 

market share and summing across all owners within each market in each time period. For 

example, if a market has 10 local owners each with 10% local market share and 1% 

national market share in 1999, the value of local-national in that market would be .01 in 

the year 1999. If that same local market then has 10 owners each with 10% local market 

share and 10% national market share in 2000, then the value of local-national would be 

.1 in the year 2000, a 1000% percent increase from 1999. Using local-national, we can 

see whether changes in presence of national conglomerates have any effect on the price 

of local radio advertising. Absent this variable, we would have no way of distinguishing 

the behavior of a locally owned station from the behavior of, say, a Clear Channel or 

Viacom-owned station in a local market. 

 Finally, we adjust for important demographic factors by including the per capita 

income and population for each market in each year covered by our study and including 

the National Real GDP to adjust for overall nationwide economic growth. Other 

demographic factors, such as age or racial composition, most likely do not vary over time 

and thus may not have significance in a fixed-effects panel approach. 

The data lends itself to a good birds-eye description of radio trends over the last 

five years. The simple chart below lists the Local HHI, national HHI, number of owners, 

CPI, and average Cost-per-thousand (CPM) listeners aged 18-49. 
10

 

Table One 

The Radio Market 1995-2000 

Local HHI National 

HHI 

Owners Consumer 

Price Index 

Average Population 

– weighted CPM 

(1st Quarter of 

following year) 

1995 2102.64 125.46 17.2 152.4 First Quarter 1996 - 6.2

1996 2151.24 191.38 16.5 156.9 First Quarter 1997 - 6.9

1997 2666.07 286.73 14.9 160.5 First Quarter 1998 - 7.8

1998 2860.73 621.39 14.0 163.0 First Quarter 1999 - 8.8

1999 2965.35 780.68 13.3 166.6 First Quarter 2000 - 10.1

2000 3084.03 1052.67 12.7 172.2 First Quarter 2001 - 11.2

Total % 

Change 

1996 - 2001 

47% 739% -26% 13% 81%

As these descriptive statistics show, the radio market consolidated considerably 

during the 1995-2000 period. The local HHI rose by an average of 47% for the 214 local 

markets we measured, and the national HHI rose by 739%. In addition, the price charged 

by radio stations to advertisers increased by 81% (68% in inflation-adjusted dollars), 

which indicates that the increased consolidation may have led to an increase in 

advertising prices. The charts below illustrate this point. 
11

Chart One: CPM and CPI

0

5

10

15

20

1995 1996 1997 1998 1999 2000 2001

CPM CPI/10

Chart Two: Local and National Concentration

0

20

40

60

80

100

120

1995 1996 1997 1998 1999 2000

(Local HHI)/100 # Owners (National HHI)/10

V. Econometric Specification and Estimation 

We estimate a panel of 214 radio markets over 5 years, using fixed-effects panel 

regression. This gives 6 observations for each market (1995, 1996, 1997, 1998, 1999, 

and 2000), for a total of 1284 observations. Since there may be unobserved heterogeneity 
12

across markets, we use fixed-effects panel regression. (Hausman tests between the fixedeffects and random-effects panel regression estimates confirm our expectation of 

unobserved heterogeneity across radio markets.) Advertising prices for the 1st Quarter of 

1996 were negotiated in the final quarter of 1995, before the passage of the 1996 

Telecommunications Act. Therefore, the 1996 Telecommunications Act provides us with 

a source of exogenous variation, so that changes in the advertising price and changes in 

the market structure coincide with our first observation. We divide the CPM by the 

Consumer Price Index to obtain the inflation-adjusted advertising price. 

We wish to examine the effects of national concentration on the price of 

advertising. However, national concentration is the same across all markets at any given 

point in time. Therefore, we cannot estimate the effects of national concentration in a 

two-way fixed-effects panel setting, because controlling for any and all changes that 

impact all markets simultaneously automatically eliminates national concentration, 

because national concentration is precisely such a change. Therefore, we estimate two 

models. The first model is a one-way fixed effects model that controls for unobserved 

heterogeneity across markets and includes national concentration. The second is a twoway fixed-effects model that controls for heterogeneity across markets and across time 

periods and excludes national concentration and National Real GDP. We estimate both 

models log-linearly4

. 
13

 

Model 1: For market i during time-period j, where i=1...214, and j=1...6. 

i ij local national nationalRGDP

localHHI nationalHHI numberofowners

realincome population ij Ln AdpriceforMorningDriveTime

ij

ij ij ij

ij ij

β β ε

β β β

β β β

+ − + + Λ +

+ + +

= + +

(ln ) (ln ) 6

(ln ) 5 (ln ) 4 (ln ) 3

(ln ) 2 (ln ) 0 1 ( )

7

 

 

Model 2: For market i during time-period j, where i=1...214, and j=1...6. 

i j ij localHHI numberofowners local national

realincome population ij Ln AdpriceforMorningDriveTime

ij ij ij

ij ij

β β β ε

β β β

+ + + − + Λ + Γ +

= + +

(ln ) 5 (ln ) 4 (ln ) 3

(ln ) 2 (ln ) 0 1 ( )

 4

 When we examined two-sided Box-Cox transformations, the log-log model could not be rejected at even a 

10% significance level. 
14

Table 2 below displays the estimation results, not including the estimates for each of the 

214 market dummy variables. 

Table 2: 

The Effects of Consolidation on Local Radio Advertising Prices 

(T-statistics in Parentheses)

 Model 1 – One-way fixed-effects Model 2 – Two-way fixed-effects 

 Ln(Price of Radio Advertising 

during Morning Drive-time) 

Ln(Price of Radio Advertising 

during Morning Drive-time) 

Ln(Population) -.19** - (2.28) -.17** - (1.96)

Ln(Real Income) .47*** (6.94) .55*** (8.02)

Ln(Local HHI) .03** (2.35) .04*** (3.84)

Ln(National HHI) -.01 (0.67) - 

Ln(# of Owners) -.04 (1.58) -.03 - (1.15) 

Ln(Local-National) -.02*** - (6.40) -.02*** - (5.29)

Ln(RealGDP) 2.74*** (12.78) - 

Constant -13.90*** -(10.19) -4.06*** - (3.79)

F-statistic 173.19*** 171.38*** 

Adjusted R-squared .97 .97 
15

N 1284 1284 

(*** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates 

significance at the 10% level) 

VI. Interpretation of the Results 

The coefficient on population is significantly negative in both models, indicating 

that increases in population lower the value of reaching an individual listener. This may 

indicate that part of the value of local radio advertising may be the ability to reach all the 

listeners in a given market (achieving “saturation”), so that increases in the number of 

potential listeners decreases the value of reaching an individual listener. Silk, Klein, and 

Berndt discuss the role that advertising agencies play in coordinating advertising across 

media. To the extent radio advertising does not reach an entire market, then the 

advertising agency may have to employ other media. To the extent these other media 

also reach viewers otherwise reached by radio, the advertising agency’s demand for radio 

time may decline. Not surprisingly, the coefficient on real income is positive and 

significant, indicating that advertisers prefer markets with wealthier listeners. 

The results indicate that increases in local concentration modestly increase the 

price paid for local advertising by national and regional advertising agencies. In both 

models, an increase in the local HHI causes a small but statistically significant increase in 

the price of local radio advertising. A doubling of the HHI in a given market would raise 

the local advertising price by 3% according to the first model and 4% according to the 

second model. This indicates that local concentration increases the market power of 
16

radio stations over national and regional advertisers in the local radio advertising market. 

The effect of the number of owners is negative but statistically insignificant in both 

models. 

 Example: Assume that the Hypothesisville local radio market has four owners, 

each with 25% market share. This yields a local HHI of 2500. Two of these owners wish 

to merge. If the merger is permitted, Hypothesisville’s local radio market would have 

three owners, one owner with a 50% market share and two other owners each with a 25% 

market share. This would increase local HHI from 2500 to 3750, an 50% increase, and 

would decrease the number of local owners from 4 to 3, a 25% decrease. According to 

model one, this 50% increase in local HHI would increase the price of advertising by 

1.5%, while the 25 percent decrease in the number of owners would increase the price of 

advertising by 1%, for a total increase of 2.5%. According to model two, the 50% 

increase in local HHI would increase the advertising price by 2%, while the 25% decline 

in the number of owners would increase the advertising price by approximately .75%, for 

a total of approximately 2.75%. Thus, both models would predict a similar increase in 

advertising price from the merger in this particular case. 

 In model one, National HHI is statistically insignificant, with much of the 

increase in advertising prices over time driven by economic growth as proxied by the 

Real GDP. In model two, there is no coefficient on National HHI or Real GDP because 

National HHI and Real GDP cannot be included in a two-way fixed-effects model. In 

both models, however, the coefficient on local-national is negative and significant, 
17

indicating that the local radio advertising prices charged to national and regional 

advertising agencies declines when a larger share of a local market belongs to owners 

with larger national market shares. The latter finding implies that national radio 

companies may sell more local radio advertising time to national and regional advertising 

agencies, which may also mean that these national companies sell less local radio 

advertising time to local businesses. 

 Using our model, we can generate the predicted values for advertising prices for 

each market. The mathematical forms of the models we have estimated are: 

Model One: .01 2.74 .04 .02

13.90 .19 .47 .03

− − −

− + − =

NationalHHI NationalRGDP Owners LocalNational

Adprice e population realIncome LocalHHI MarketDummy

Model Two: .03 .02

4.06 .17 .55 .04

− −

− + + − =

Owners LocalNational

Adprice e population realIncome LocalHHI MarketDummy TimeDummy

 Let us use the Phoenix, Arizona radio market in the year 2000 as an example. In 

2000 Phoenix, Arizona had a population of 1,414,000, a per capita income of 27,564.115

, 

a local HHI of 1,412.84, a National HHI of 1052.676

, a GDP of 9.8 billion7 , 28 owners, 

and a local-national measure of .06. Phoenix’s market dummy coefficient in model one 

is .009. Plugging these into model one gives a predicted advertising price of 8.99 per 

CPM for Phoenix in the year 2000. For model two, all values stay the same except 

Phoenix’s market dummy is now -.04, the constant is -4.06, and the time dummy for the 

 

5

 With a CPI of 172.2, this gives a real income of 160.07. 

6

 The National HHI is .105267 in the data set. I adjust for this in calculation. 

7

 Dividing this by the CPI of 172.2 gives a Real GDP of 57.05. 
18

year 2000 is .40. Plugging these into model two gives a predicted price of 8.00. The 

actual price of a CPM in Phoenix during the year 2000 was 8.7. Thus the models 

perform reasonably well in predicting advertising prices. We could also use the models 

to predict the effects of a radio merger on prices charged to national and regional 

advertising agencies for local radio advertising. 

Conclusion 

 We have estimated the effects of consolidation on local radio advertising prices 

charged to national and regional advertising agencies. The 1996 Telecommunications 

Act provided a source of exogenous variation that allowed us to estimate the effect of 

concentration on local advertising rates. This study represents a unique attempt to 

estimate the effect of concentration on the price of local radio advertising. We also 

incorporate the effects of national consolidation, another first. Overall, we find that local 

consolidation appears to increase the prices paid by national and regional advertising 

agencies for local radio advertising. At the local level, the explanation appears to be that 

consolidation does create more market power, by allowing the exercise of increased 

unilateral market power. From 1996 to 2001, population-weighted HHI increased by 

73%, while the population-weighted number of owners fell by 26%. When we plug these 

numbers into either model, we find that local concentration accounted for approximately 
19

3-4% out of the 68% increase in real advertising rates8

. At the local level, greater 

ownership by large national radio firms led to lower local radio advertising prices for 

regional and national advertising agencies. Thus, national consolidation may have been 

advantageous from the perspective of national and regional advertisers. 

 Again, the advertising market is not the only market in radio. Radio listeners also 

comprise an important group of consumers, whose interests may not be the same as those 

of advertisers. In fact, if listeners dislike advertising, then the reduced quantity of 

advertising resulting from radio firms’ exercise of market power may actually benefit 

listeners. Future studies should examine listener data in these 214 markets during this 

time period, to see if higher prices charged to advertisers led to greater listenership. This 

would give researchers and policymakers a more complete explanation regarding the 

effects of local and national consolidation in the radio industry. 

 

8

 This would imply that changes in local concentration explain approximately 4-5% of the 68% increase. 
20

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