FEDERAL COMMUNICATIONS COMMISSION

Media Ownership Working Group 

Consumer Substitution 

Among Media 

By Joel Waldfogel 

September 2002 

2002 - 

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1

Consumer Substitution among Media 

Joel Waldfogel 

The Wharton School 

University of Pennsylvania 

This research has been supported by the FCC and has been conducted by the author in his 

capacity as a consultant to the Commission’s Media Ownership Working Group. I thank 

Lisa George and Felix Oberholzer-Gee for the use of resources developed for other joint 

work on related projects, and I received helpful comments from Paul Gallant and Jon 

Levy on an earlier draft. The work with Oberholzer-Gee was supported by the Ford 

Foundation. All errors are my own.
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Consumer Substitution among Media: Executive Summary

 The past decade has seen a number of major changes in the availability and use of 

information through media channels. The use of some media – cable television, the 

Internet, and weekly newspapers – has grown, while the use of others – radio, broadcast 

television, and daily newspapers – has declined. Our question is whether the changes in 

availability or use of some media have brought about changes in the availability or 

consumers’ use of other media, or whether different media serve as substitutes for one 

another for information consumers. 

 This question is important because FCC media ownership policies are predicated 

to varying degrees on the extent of substitutability of media for various purposes - news, 

entertainment, etc. This study examines the extent of substitutability across media for 

both of these purposes using a variety of demand and supply measures. 

 The specific tasks of this study are twofold. First, we survey the media landscape 

since the mid 1990s. To accomplish this we have assembled aggregate data on both the 

availability of media outlets and their use over time, as measured by listening, viewing, 

circulation, etc. across US local media markets. We then turn to our main task, 

documenting the extent of consumer substitution across media. In Part I we do this with 

aggregate data, by examining the relationship between changes in the availability and use 

across various media. For example, has broadcast TV use declined more in the markets 

where Internet use has increased most quickly? 

In Part II, we reexamine this question using a large cross sectional data based with 

information on individuals’ use of various different media. These data allow us to 

observe whether a person using one medium also uses another, for news and other 
3

purposes. We explore substitution with these data by asking, for example, whether 

individuals using daily newspapers more use television less. 

 What do we find? Standing back, there is clearest evidence of substitution 

between Internet and broadcast TV, both overall and for news; between daily and weekly 

newspapers; and between daily newspapers and broadcast TV news. There is also 

evidence of substitution between cable and daily newspapers, both overall and for news 

consumption; between radio and broadcast TV for news consumption; and between the 

Internet and daily newspapers for news consumption. There is little or no evidence of 

substitution between weekly newspapers and broadcast TV, or between radio and either 

Internet or cable. There is also some indirect evidence of substitution in the greater use 

of national media by groups less targeted by local media. 

 This study leads to several conclusions. First, we can reject the view that various 

media are entirely distinct. As noted above, certain media appear to compete with each 

other for consumers’ attention. Second, the study provides evidence of substitution by 

consumers between and among certain media outlets. It cannot completely answer the 

question of whether substitution is sufficiently effective that all media should be 

considered substitutes for news and information purposes. Existing research, however, 

may provide useful clues. While entertainment provided through media channels is an 

end in itself, news makes it easier for citizens to know what is at stake in electoral 

contests and, in turn, can make them more likely to vote. If substitution were complete, 

then the decline of local daily newspapers would be offset by increased use of other 

media. The civic behaviors affected by media consumption would also be unaffected by 

changes in availability or use of any particular medium. Yet, existing research on media 
4

consumption and voting – reviewed in the study conclusion – suggests that, even if 

substitution operates, it is not complete in this sense. 
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Part I: Evidence from Aggregate Data 

 The past decade has seen a number of major changes in the availability and use of 

information through media channels. The use of some media has grown, for example 

cable television, the Internet, and weekly newspapers. The use of other media, including 

radio, broadcast television, and daily newspapers, has declined. An important question 

that we seek to address in this study is whether the changes in availability or use of one 

medium bring about changes in the availability or use of other media. In short, we are 

interested in whether different media serve as substitutes for one another. This is relevant 

to FCC policies which are predicated to varying degrees on the extent of substitutability 

across media for various purposes – news, entertainment, etc. This study examines the 

extent of substitutability across media both for both of these purposes using a variety of 

both demand and supply measures. 

 The specific tasks of this study are twofold. First, we will survey the media 

landscape since the mid 1990s. To accomplish this we have assembled aggregate data on 

both the “supply” of media outlets and the “demand” for the content provided through 

these outlets, as measured by listening, viewing, circulation, etc. across US local media 

markets. We begin by simply documenting levels and trends within each medium. Are 

television viewing, radio listening, and newspaper circulation rising or falling? 

 We will then turn to our second broad task, to document the extent of consumer 

substitution across media. We will address this question with two different sorts of data. 

First, in Part I of the study we will examine aggregate data on media outlets and use over 

time. In Part II we will turn to a large individual cross sectional data set on media use. 
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 We would ideally like to know the answers to a number of questions: First, how 

does the supply of outlets in each medium respond to supply of outlets in other media? 

Second, how does the demand for information through each medium respond to the 

availability of outlets in other media? Finally, how does the use of each outlet relate to 

the use of other outlets? 

These are difficult questions to answer, in particular to draw tight causal 

inferences. It would strain credulity, for example, to draw substitution inferences from 

the cross sectional relationship among markets’ outlets in different media. For example, 

larger markets tend to have more outlets in all media; and this is not because these outlets 

are complements. Rather, it is because larger markets make more outlets viable in all 

media. An alternative strategy for drawing substitution inferences is to use the timeseries variation in outlets to ask, for example, whether there is greater decrease in 

television viewing in markets with larger increases in Internet use. 

This strategy has substantial strengths. To the extent that outlets, or use, of 

different media in each market are driven by fixed unobservable factors, examining 

changes allows these factors to be “differenced out.” This, in turn, makes it possible to 

infer causal relationships among the use or availability of outlets in different media. At 

the same time, however, this strategy does not overcome all obstacles. If consumers in a 

place experience a change in their level of interest in information, this may bring about a 

change in their use of multiple media. In particular, this will tend to bring about a 

positive relationship among the changes in supply and demand for different media, as 

well as in their levels. This sort of effects makes it appear that different media are 

complementary. That is, this induces a bias away from detecting substitution among 
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media, if that substitution operates. As a practical matter, then, this approach is 

conservative, and the true extent of substitution may be greater than any we can 

document. 

 Bearing in mind the limitations of the approach, the relationships are still quite 

interesting to examine. In fact, we know very little about how consumers substitute 

among different media. Our ignorance is due to two factors. First, as discussed above, 

inferring causality is difficult. Second, the question has received little study. 

Researchers and policy makers have devoted significant attention to whether advertising 

in one medium is a substitute for advertising in another, but there little research (to my 

knowledge) on whether information provided through one medium serves as a substitute 

for information provided through another.1

 The question is important. If consumers 

substitute information across media, then the market for information may extend across 

media, raising questions about regulation of outlets within media. The importance of the 

question, in conjunction with our general ignorance about the answer, amply justify the 

adoption of sensible approaches to studying the question. This is the spirit in which we 

proceed. 

II. Theoretical Background 

Consumers seek information (news and weather, etc) and entertainment from 

media products. Suppose there are five relevant media (television, radio, newspapers, 

etc.), and each can be divided into news and entertainment components. Then there are 

ten products to examine. The usual economic way to conceive of this problem is through 

consumer theory: each consumer’s demand for each of the ten products depends 
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(negatively) on the price of the own product and, if the other products are substitutes, 

negatively on the other products’ prices. While this characterization is very helpful for 

organizing ideas, it is not literally useful in our context. Content is unpriced (free) on 

radio, broadcast television, and (for the most part) on the Internet. And while newspapers 

have subscriber prices, these prices vary little across markets, largely because newspapers 

rely primarily on advertising rather than subscriptions for revenue. 

 In this context the more useful variation is in availability of outlets within a 

medium rather than in the price that consumers pay for content. It is well known that 

larger markets have more local media outlets within each medium than small markets 

(see Berry and Waldfogel, 1999a; Waldfogel, 1999, 2001; George and Waldfogel, 2000). 

There are also changes over time in the numbers of outlets (see Berry and Waldfogel, 

2001 for evidence about radio following deregulation). Conceptually, the change from 

having no outlets in a medium to having one is a price reduction from infinite levels to 

some (perhaps unmeasured) finite amount. Changes among positive numbers of outlets, 

from say one to two in a medium and a market, are similar in spirit. Such changes 

enhance the attractiveness of each consumer’s most attractive alternative and in that sense 

can be viewed, metaphorically at least, as reducing the “price” of consumption in that 

medium. Given our context, then, we propose to study substitution chiefly by examining 

the relationship between use in one medium and availability of outlets in other media. If 

two media are substitutes, then greater availability of outlets in one medium will reduce 

the use of the other medium. 

 While this question is not much studied, there is some evidence in existing 

research on aspects of substitution. Berry and Waldfogel (1999a) provide a study 

 1

 We discuss related research in part I, section II below. 
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documenting high levels of substitutability across outlets within the commercial radio 

medium. Additional outlets draw most of their listeners from other stations, suggesting 

that stations are rather close substitutes for one another. Berry and Waldfogel (1999b) 

examine the extent of substitutability of public and commercial radio broadcasting news, 

classical music, and jazz. Although that study, too, is within medium, it is relevant here 

both because it documents substitutability and also because its approaches are adopted 

here. That study infers substitutability of public and commercial radio stations from a) 

the relationship between public and commercial radio outlets and b) the relationship 

between commercial listening and the numbers of commercial and public radio stations in 

the format. The study documents that markets with more commercial classical stations 

have greater commercial classical tendencies, while commercial classical listening is 

lower in markets with more public classical stations, all else constant. The latter result 

suggests that listeners view public and commercial classical radio stations as substitutes. 

 Other existing work provides evidence of substitutability of different types of 

media outlets. George and Waldfogel (2002) document that readers view national 

newspapers (the New York Times) as a substitute for local daily newspapers. In markets 

with greater increases in Times circulation, there is a larger decrease in local paper 

circulation in zipcodes containing educated persons targeted by the Times. This indicates 

that national papers are substitutes for local ones. 

 Finally, there is some indirect evidence of cross-medium substitution. It is well 

documented in a variety of studies that there are more local media outlets (and 

programming) in larger markets. Moreover, isolated groups face more outlets or 

programming of interest as they are in markets with more persons sharing their 
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preferences (see Waldfogel, 1999, 2001; Siegelman and Waldfogel, 2001; George and 

Waldfogel, 2000). In light of this, if consumers can use nonlocal media to substitutes for 

the relative paucity of local media, then we would see greater use of nonlocal media 

(such as the Internet or cable television) in markets where consumers were more isolated. 

Sinai and Waldfogel (2001) documents that in spite of their lower tendency to connect to 

the Internet overall, blacks are more likely to connect as they are more isolated in markets 

with proportionately few blacks. This suggests that isolated information consumers 

substitute nonlocal media for local ones. 

 Finally, there is evidence elsewhere to support the supposition that media matter 

for civic participation. Oberholzer-Gee and Waldfogel (2001) document that the 

presence of group-targeted media outlets (radio stations and weekly newspapers) affect 

the tendency for blacks to vote. George and Waldfogel (2002) find that in markets with 

greater distribution of the New York Times, not only do the targeted (educated) 

consumers purchase local papers less. They also come to vote less in local elections as 

Times penetration grows. 

III. Data 

 Our basic data set in this part of the study is a panel with repeated observations on 

the number of outlets in each medium in each market, as well as measures of the use of 

each medium. Our data come from five sources (BIA’s Media Access Pro, Duncan’s 

American Radio, the Current Population Survey’s Computer and Internet Use 

Supplements, Burrelle’s Media Directory, and the U.S. Census) that collect data in 

different ways. Still, we ultimately have multiple years of use data, by market, for 
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broadcast television, cable, radio, daily newspapers, weekly newspapers, and the Internet. 

We have multiple years of outlet availability data for broadcast TV, radio, and daily and 

weekly newspapers. We describe these data in detail, and by medium, below. 

1. Television 

The television data are from BIA Media Access Pro. BIA provides supply data, 

the total number of stations in each market, by type (UHF vs. VHF, network vs. 

independent vs. public). BIA also provides market aggregate television viewing, derived 

from Nielsen data, for each of 210 DMAs. Aggregate HUT (households using television) 

is provided by year, 1994-2000. BIA also provides daypart specific viewing data (based 

both on HUT and total viewing) for a recent window of more frequent observations 

(quarterly, November 1999 to July 2001). 

2. Daily and Weekly Newspapers 

 We document the number of daily newspapers available in each DMA using 

Burrelle’s Media Directory for 1993 and 1999. Burrelle’s also provides a measure of 

total circulation for each daily. We aggregate across products in each DMA to arrive at 

total circulation for dailies and weeklies in each market. 

3. Radio 

 We have data on the number of radio stations, and their programming formats, in 

each of roughly 250 Arbitron markets, in 1993 and 1997. These data are derived from 

Duncan’s American Radio (1994, 1998), based on Arbitron listening data as well as 
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Duncan’s classification of programming formats. Notably for our purpose, we can 

distinguish news stations (“News” and “News/Talk”) from other sorts of stations. 

 Duncan also provides AQH listening data, both for individual stations, as well as 

for entire markets. We employ the data based on the Arbitron Spring surveys in the 

respective year. Arbitron markets do not map neatly into DMAs, so we have undertaken 

effort to create a mapping that allows the radio data to be linked with data at the DMA 

level. 

4. Internet Use 

 In 1997, 1998, and 2000 the Current Population Survey includes questions about 

Internet use. We focus on the following five questions, available in all three of these 

surveys: 1) whether the individual has Internet access at home, 2) whether the individual 

has Internet access at work, 3) use of the Internet for news, 4) use of the Internet for 

information, and 5) use of the Internet for email. 

 For this medium we cannot clearly distinguish availability of outlets from use of 

the medium. Thus, we really have only a “demand” measure for this medium. 

Moreover, the geographic identifier in these data is the MSA. We have created a 

mapping that allows CPS data to be linked to DMA data. 

 

5. Cable Takeup 

 BIA reports the percent of households with cable, by year, 1994-2000, at the 

DMA level of geography. 
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6. Demographics 

 We obtain demographic data from various sources, derived from the 1990 US 

Census. 

IV. Trends within each Medium 

 In this section we document trends in media supply and use in each medium. 

Following that, we examine the comovements in media supply and use in our attempt to 

learn about substitution among media. The latter exercise requires linking data on outlets 

and use of various media. While simple in principle, this task is quite complicated in 

practice because the data on various media are collected for different levels of geography. 

For example, the BIA television and cable data, as well as the Burrelle’s newspaper data, 

are reported by DMA. The CPS data on Internet use are individual-level data, and the 

individual records include MSA identifiers. The Arbitron radio data reported in 

Duncan’s American Radio, are reported by Arbitron metro area. These are similar, but 

not identical, to MSAs. We have had to construct mappings linking MSAs and Arbitron 

metro areas to DMAs. 

The linked multi-media dataset covers a subset of geographic areas for which 

linking is feasible (roughly 140 of 210 DMAs). To address concerns about whether this 

sample is representative, we compare the trends in each medium’s use in the linking 

sample against the measures for the medium’s full sample. 

1. Television 
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BIA reports two measures of the number of stations available in a DMA, the total 

number of stations and the number of rated stations. Neither variable is reported 

historically. BIA also provides two breakdowns of the number of stations: UHF/VHF 

and network/independent/public. The sums of each of these two breakdowns yield 

approximately the number of rated stations. Even though they do not yield precisely the 

current number of rated stations, we still view the change in the total number of UHF and 

VHF stations, and the change in (network + independent + public) stations over time as 

reasonable reflections of the time pattern of local broadcast TV station availability. Table 

1 reports trends in the average numbers of stations of each type in each DMA. The top 

panel includes the full sample2

 while the bottom panel includes only the DMAs in the 

linked sample. Trends in both are quite similar: the total number of stations has 

increased slightly, from 8.4 in 1995 to 9.7 in 2000 (network + indep. + public measure in 

the linked sample). UHF stations have increased more quickly than VHF. The number 

of network-affiliated stations has increased as networks outside the traditional three 

majors have increased their reach. Almost by construction, this decreases the number of 

independents. The number of public stations has held steady. These trends are clearly 

visible in both samples. 

Table 2 reports patterns of television and cable use. The percent of households 

with cable has increased steadily, from 63 percent in 1994 to 68 percent in 2000 (linked 

sample). We have an overall annual measure of television viewing (excluding cable) 

since 1994. The measure is “households using television.” We also have quarterly 

 

2

 The full sample includes 207 of the 210 DMAs in the US. Three DMAs are excluded because of missing 

data. 
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measures of television viewing by daypart, from November 1999 to July 2001. See Table 

3. 

Television viewing (not including cable channels) has declined steadily over the 

same period, from 37.3 percent in 1994 to 36.8 percent in 2000. The quarterly measures, 

in Table 3, indicate both a strong seasonal pattern – higher use in November and 

February, lower in May and July – and the same secular decline as the annual HUT 

figures. This decline appears both for the dayparts associated with news as well as all 

others. Because these rating figures do not include time spent watching cable networks, 

they do not capture overall viewing behavior. 

2. Radio 

As with television, the mid 1990s saw an increase in the number of outlets, as 

well as increase in the number of distinct programming formats on the air across 230 US 

markets. There were an average of 21.7 stations available in each market in 1993 and 

24.8 in 1997. Of these, 15.0 broadcast from inside the metro area in 1993 and 16.6 in 

1997. 

Table 4 shows the distributions of programming formats in 1993 and 1997. The 

average number of formats available in each market grew from 11.6 in 1993 to 15.0 in 

1997 (see Berry and Waldfogel, 2001). AQH listening declined steadily from 16.8 

percent in 1993 to 15.7 percent in 1998. 

We can separately document the evolution of commercial news radio3

. As Table 

5 shows, the average number of inside (total) news stations was 0.46 (0.78) in 1993 and 

 

3

 These data exclude public radio. See Berry and Waldfogel (1999b) for information about public and 

commercial radio in 1993. 
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0.55 (0.94) in 1997 (using the overall sample).4 Perhaps more important than how many 

is the question of inside news station presence. Among the 230 markets, the number of 

markets without an inside news station declined from 142 in 1993 to 135 in 1997. 

Interestingly, 28 markets went from having no inside news stations in 1993 to having one 

or more in 1997, while 21 markets went from having one in 1993 to having none in 1997. 

Table 5 also shows how the number of formats available in each market increased 

from 11.6 in 1993 to 15.0 in 1997, consistent with Table 4. 

3. Newspapers 

 The average numbers of daily newspapers per DMA declined from 8.05 in 1993 

to 7.85 in 1999, while over the same period the number of weekly newspaper outlets 

grew from 44.18 to 46.21. Average daily circulation fell from 309,579 in 1993 to 

295,950 in 1999. Weekly newspaper circulation, on the other hand, grew from 531,693 

in 1993 to 728,804 in 1999. Similar patterns appear in the linked sample. See Table 6. 

 

4. Internet 

We lack data on the availability of the Internet, either on the availability of ISPs 

or, more important, on the number of local and national sites of interest to users. On the 

first question, since we are examining metropolitan areas, we assume that connection 

services are available everywhere in the sample by 1997 (see Downes and Greenstein, 

2002). We have Internet connection data for 1997, 1998, and 2000. The Internet was 

virtually nonexistent prior to 1994, so we can document the entire history 1994-2000. 

 

4

 By “inside” we mean stations broadcasting from inside a metro area. 
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As Table 7 shows, Internet use has grown rapidly, to 15.1 percent of households 

with home access in 1997 to 30.3 percent in 1998, and to 45.9 in 2000. The Table also 

shows the fraction of individuals using the Internet for email, news, and information, the 

latter two representing ways in which Internet use might substitute for the use of 

traditional media. Patterns are virtually identical in the linked sample of markets. 

5. Summary 

 The mid 1990s has been a period with a shifting media landscape. Use of radio, 

daily newspapers, and traditional television have declined, while the use and availability 

of other media have increased. Cable television use has grown, as has the availability 

and use of weekly newspapers and the Internet. These comovements may reflect 

substitution, but the separate trends in each media cannot answer this. The question, 

addressed in the next section, is whether one medium grows in the places where others 

decline. 

V. Substitution across Media 

1. Availablity of Media Outlets 

 It is well documented that larger markets offer more local media products. This 

relationship holds for radio, television, daily newspapers, and even to some extent for the 

websites on the Internet.5

 These relationships are clearly visible in our data as well. 

Figure 1 shows the relationship between the numbers of media outlets in a DMA and 
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population for radio, television, daily newspapers, and weekly newspapers. The figure, 

on a log scale for ease of interpretation, clearly shows positive relationships between 

market size and the number of local outlets in each medium. In this figure we use radio 

and television outlets in 1997 and newspaper outlets in 1999. The cross sectional 

relationships for other years are nearly identical. 

2. Use of Media and Availability of Media Outlets 

Our data allow us to examine the relationships between outlet availability and 

medium use in two ways. First, we can examine the cross sectional relationships directly. 

Table 8 presents regressions of our use measures on outlets for the four media with outlet 

data (radio, television, and daily and weekly newspapers). Coefficients are positive and 

significant for all media. These regressions are vulnerable to the concern that the number 

of outlets is endogenous, or that more outlets enter in markets with greater tastes for the 

outlet in question. There are two obvious solutions to this problem, instrumental 

variables (IV) and panel data. First, the IV solution to this problem is an instrument 

determining the tendency for outlets to enter but that has no direct effect on the tendency 

to join the audience. Market size, measured by population, is a natural choice for local 

media and one that has been employed elsewhere in media entry studies. The latter half 

of Table 8 reports regressions of use on available for the four media, now instrumenting 

outlets with a population measure. The coefficients of interest are again positive, 

indicating that additional outlets within a medium attract persons to their audiences. 

 5

 See Berry and Waldfogel (1999b) and Waldfogel (1999) for radio evidence, Waldfogel (2001) for 

television evidence, George and Waldfogel (2000) for evidence on daily newspapers, and Sinai and 

Waldfogel (2001) for evidence about the Internet. 
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For Internet and cable we have no direct measure of local outlets. Still, we can 

examine the relationship between use and market size. Table 9 shows the relationship 

between medium use and market size for all of the media. Interestingly, while the local 

media have positive and significant relationships, cable and Internet use have essentially 

no relationship with market size. For these media, which do not originate locally, market 

size has a different natural interpretation. Rather than proxying the amount of content 

available locally within the medium, market size here measures the availability of content 

in other media. 

The difference between the relationship between medium use and market size in 

local and nonlocal media thus provides our first piece of evidence, albeit indirect, about 

substitution. While the tendency to use local media increases in market size, the 

tendency to use the national media, relative to the tendency to use local media, is larger 

as markets are smaller. This suggests that in smaller markets, Internet and cable serve as 

substitutes for newspapers, local television, and radio. One obtains similar but even 

stronger relationships if one examines Internet use relative to television use. Define ∆I_TV

as the Internet use – broadcast TV use in a market. Then a regression of ∆I_TV on log 

population yields a negative and significant coefficient. 

We can also approach the question of whether outlets affect use employing the 

within-market variation in the number of outlets over time. Table 10 presents market 

fixed effects regressions of use on outlets, including year dummies as well as market 

fixed effects. We obtain positive and significant coefficients on outlets in the television 

and daily newspaper regressions. Coefficients are insignificant in the other regressions. 

One should not infer the absence of relationships from the absence of results here. There 
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simply is not a great deal of variation across markets in the change in the number of 

outlets over time, making it difficult to infer the relationships in question. 

We take the weight of evidence in this subsection to indicate that additional 

outlets in a medium attract more of the population into the medium’s local audience. In 

addition, the difference between the relationship between market size and medium use for 

national and local media suggests substitution. 

3. Evidence on Substitution across Media from Cross-Media Relationships 

We seek to measure whether different media are substitutes for one another using 

an approach related to our approach (above) to whether outlets attract use within media. 

Here, by contrast, the question is whether the availability of outlets in other media 

reduces the use of this medium. There are a variety of approaches one might adopt for 

this question in principle. For example, one might examine the cross section of markets 

at a point in time, asking whether markets with more, say, daily newspapers, have lower 

use of some other media. The endogeneity problems mentioned above are severe here. 

Larger markets are going to have more outlets in all media, giving rise to positive 

correlations among media outlet availability as well as use. Indeed, Table 11 reports the 

cross sectional correlations significant at the 10 percent level or higher among use and 

outlet measures, and they are all positive (and most are significant at the 5 percent level). 

In principle one could address this problem with instruments. This approach 

requires an exogenous source of variation in the supply of each medium’s outlets. Note 

that population, while it works for each individual medium, cannot simultaneously be 
21

employed for multiple media. Since we lack instruments for each medium, this approach 

is not available. 

We do have longitudinal data, however. While the number of outlets in each 

medium is endogenous to market size, the change in the number of outlets is not so 

clearly endogenous. Thus, we propose to draw inferences about substitution from the 

correlations between the changes in the availability and use of outlets in various media. 

This approach is in principle vulnerable to analogous endogeneity concerns (i.e. the kind 

of market with a growing number of TV stations is likely to be growing in other 

dimensions as well that simultaneously lead to growth in outlets in other media). 

Fortunately, we can ascertain the direction of likely bias: these concerns lead to positive 

relationships. Hence, any evidence of negative correlations is more strongly suggestive 

of substitution. 

Table 12 reports the statistically significant correlations among changes in supply 

and use of outlets in various media. A striking feature of the Table, in contrast to the 

correlations among levels, is the presence of negative – as well as positive – correlations. 

The following correlations are negative and significant at the 10 percent level or higher: 

Radio outlets and cable use, 

Radio news outlets and: Internet use, overall and for news, 

Daily paper outlets and: weekly paper outlets and cable use, and 

Daily paper use and: and cable use. 

The largest and most significant negative relationships in the Table are between daily and 

weekly newspapers. 
22

 One shortcoming of the direct correlations is that they cannot simultaneously 

account for changes in multiple media. For example, if the numbers of outlets in both 

radio and television are changing similarly, then their correlations with, say, newspaper 

use will both reflect the effects of both the change in radio and the change in television. 

One way around this problem, in principle, is to employ a multiple regression. That is, 

we can regress, say, the change in daily newspaper use on the changes in the numbers of 

outlets of each other medium. The coefficient on each variable shows the relationship 

between that variable and the dependent variable, holding other variables constant. 

 While appealing in principle, this approach has a shortcoming that merits 

discussion: if the explanatory variables are highly correlated, then it is difficult to isolate 

the effect of each variable separately. This said, we proceed with three sorts of 

regressions. First, we present regressions of changes in the numbers of outlets in each 

medium on changes in the numbers of outlets in each of the other media (“outlets on 

outlets”). This regression can pick up substitution in the sense that if one medium 

expands in an area sufficiently to not only draw audience from another but cause exit (or 

forestall additional entry) in another, then we can detect substitution as a negative effect. 

Second, we run regressions of the change in the share of population using each medium 

on the changes in the numbers if outlets in each medium (“use on outlets”). This 

regression can pick up substitutability short of cross effects that induce exit and is, a 

priori, the most appealing approach. However, recall that we do not have outlets 

measures for all media. For cable and Internet we have only use measures; and we would 

like to use the change in these media as explanatory variables. Third, we run regressions 
23

of the change in the share of population using each medium on the changes in the share 

of population using each other medium (“use on “use”). 

 Bearing all of our concerns in mind, we proceed to Tables 13-15. Table 13 

presents regressions of the changes in number of outlets in each medium on changes in 

the numbers of outlets in other media (for which outlet data are available). The only 

consistent relationship to emerge from this table is the relationship between daily and 

weekly newspapers. Both coefficients are statistically significant. A one-paper decrease 

in dailies is associated with an eight-paper increase in weeklies. The reverse relationship 

is not entirely symmetric: a one-paper decrease in weeklies is accompanied by a 0.02 

paper reduction in dailies. For each regression we report a test of the hypothesis that all 

cross effects are zero. We reject this hypothesis only for dailies and weeklies. 

 Table 14 proceeds to the relationship between the change in use of each medium 

and the change in the number of outlets in the medium as well as other media. The owneffects are fairly consistently positive. The only negative cross-effect significant at the 5 

percent level is the coefficient on radio stations in the cable use regression (column 6). If 

one adopts a more liberal cutoff (a negative coefficient with a t-statistic exceeding 1 in 

absolute value), the relationships suggesting substitution are: 

TV stations and: cable use, Internet news use 

Radio stations and: TV viewing, daily and paper circulation, cable use; 

Radio news stations and Internet news use; 

Daily papers and: radio listening, cable use; 

Weekly papers and radio listening. 
24

Based on results in Table 14, there is some reason to doubt the no substitution view. 

Formally, however, we reject the zero cross effects hypothesis only for the cable and 

Internet news use regressions. 

 Table 15 reports the use on use regressions. The only negative coefficients that 

are significant by conventional standards are the coefficient on the change in cable use in 

the daily paper regression and its inverse (the coefficient on the change in daily paper use 

in the cable use regression). Other coefficients are suggestive of substitution. Using the 

liberal cutoff of 1 (in absolute value) for t-statistics, one can see suggestive evidence of 

substitution in the following relationships: 

Cable use and weekly papers, 

Radio news use and TV use, 

Radio news use and Internet news use, 

Cable use and weekly paper use, and 

Internet news use and TV use. 

In spite of these suggestions of substitution, we formally reject the hypothesis of zero 

cross effects for cable use only. 

VI. Conclusion to Part I 

We conclude our analysis of the aggregate data with the observation that there is 

some evidence of consumer substitution across media. This raises questions about 

whether media are sensibly viewed in isolation for policy purposes. We delve deeper 

into this question with our analysis of individual data on media use in Part II. 
25

Part II: Evidence from Individual Data and Study Conclusion 

 Do consumers view different media as substitutes for one another? This study 

examines this question using a large cross sectional data based with information on 

individuals’ use of various different media. This study is a companion to the aggregate 

data study in Part I. 

 The advantage of these micro data is that they literally allow us to observe 

whether a person using one medium also uses another. The dataset is large (nearly 

180,000 observations) and detailed, containing information about both overall use and 

use for news in particular. These data therefore allow us to explore substitution by asking 

whether, for example, individuals using the newspapers more use television less. Or 

whether individuals using more radio news watch less television news, etc. Despite the 

substantial appeal of these data, consumption data have the drawback that individuals 

who use one medium heavily may also use others heavily. Indeed, in the companion 

study the cross sectional relationship among the uses of different media (across markets) 

revealed strong positive correlations masking possible substitution. 

Our strategies for overcoming these problems are threefold: First, we will control 

for individual characteristics. Second, we will examine not only the use of each medium 

(and the use for news vs. other motivations) but also the use of each medium for news 

relative to its use for other purposes. Third, we will also employ an indirect approach to 

testing for substitution that avoids these problems: In particular we will ask whether 

persons facing little local media content of interest turn instead to national media (cable 

and Internet). 
26

We proceed as follows. First, we describe the data. Then we descibe patterns of 

use of each medium. Third, we examine substitution. Then we will summarize in a way 

that synthesizes results from the companion study using aggregate data. 

I. Data 

Data for this part of the study are drawn from Scarborough Research. The version 

of the Scarborough Prime Next dataset employed in the study has information on the 

media and product consumption patterns for each of 178,784 individuals surveyed in the 

latter half of 1999 and the first half of 2000 in 66 large US markets (see 

http://www.scarborough.com/primenext/). The Primenext dataset has information at the 

individual level on the use of multiple media. 

The Scarborough data set contains a large number of questions about media use. 

Here we describe the available measures, by medium. 

1. Newspapers. We have a binary variable indicating whether a person reads 

any daily newspaper. In addition respondents report which newspaper section 

they regularly read (news, business, classifieds, comics, entertainment, food, 

sports, TV or radio listings). 

2. Television. Respondents report which half hours of television they watched 

on each of 11 networks during one week during the 1999-2000 television 

season.6 We aggregate these half hours in various ways, including to total 

half hours. In addition, by linking the Scarborough data with local television 

 

6

 The eleven networks are ABC, CBS, Fox, NBC, PAX, Univision, Telemundo, WB, UPN, Independent, 

and PBS. 
27

schedules for the markets, we are able to construct measures of local and 

national news viewing (between 4 and 12 PM) for individuals in the sample.7

3. Cable and Satellite. Respondents report whether they have cable. If they 

have cable, they report what sort of service they have (basic, expanded basic, 

premium). In addition, respondents indicate whether they have satellite 

service. Respondents also report which cable channels they regularly watch. 

We calculate the number of cable channels regularly viewed by sample 

individuals as a crude measure of the intensity of cable use. Because some of 

these are dedicated news channels (CNN, Fox News, MSNBC, CNBC), we 

can also calculate intensity of cable use for news as the number of dedicated 

news channels regularly viewed. 

4. Radio. Respondents report which of the radio formats they listen to. The 

formats, which are listed in Table 4, include three news-related categories 

(“all news,” “news/talk/information,” and “business”). We calculate a 

measure of radio use for news as the number of news formats regularly used. 

5. Internet. Respondents report whether they have Internet at home and whether 

at work. In addition, they report how they use the Internet at home and at 

work (whether they use it for each of the use categories in Table 6). A 

number of these uses are news-related (“local information,” “news,” 

“newspaper site,” “other newspaper site,” and “read a newspaper”). This 

allows us to calculate the number of news-related Internet uses for each 

individual along with the total number of uses. Finally, respondents indicate 

 

7

 The schedule collection process is fairly time-consuming. Since most news is aired between 4 and 12 

PM, our restriction allows us to save much effort without losing much information. 
28

how many hours they spend on the Internet per week (using a categoric 

varable that takes values of none, 0-1 hour, 1-5 hours, 5-10, 10-20, and 20+, 

with which we assign cell midpoints and 20 for “20+”) . 

In sum, we have separate measure of overall use and news use for television, radio, 

Internet, and cable. 

II. Media Use 

1. Univariate Characterizations 

a. Newspapers 

Table 1 reports that 53 percent of the sample reports reading a daily newspaper. 

Of those reading a paper, 91 percent report reading the news section. Newspapers are 

clearly and important medium for the communication of news, as opposed to the delivery 

of entertainment. Other popular sections include entertainment (69 percent), business 

(62), comics (61), and sports (61). Respondents not buying a paper also report the 

sections they regularly read, and their tendencies to read each section are lower. 

b. Television 

The average number of weekly half hours of television viewed on the networks is 

35.5. Of these, 5.3 half hours are broadcast news (viewed between 4 and 12PM). 73 

percent of households have some sort of cable television (23 percent have basic, 29 

percent have extended basic, and 22 percent have premium service). 14 percent of 

households have satellite TV. 80 percent have either cable or satellite. Individuals 
29

(including those without cable) watch an average of 8.4 cable channels regularly. Of 

these, 0.8 are news channels. 

c. Radio 

In the sample 89 percent of persons report listening to at least one radio format 

regularly. Persons in the sample reports listening to an average of 2.3 radio formats. See 

Table 3. The 54 formats available are listed in Table 4. The most popular among these 

formats are news/talk/information (23 percent of persons listen), country (21), adult 

contemporary (18), oldies (15) contemporary hit radio (14), album oriented rock (12), 

and classic rock (11). Two formats that attract listeners in the sample are oriented 

toward news: news (5.4 percent) and news/talk/information. Roughly a quarter of 

persons get some news from radio. Average persons listen to 0.28 radio news formats. 

d. Internet 

 As Table 5 indicates, just over half of people in the sample (55 percent) have 

Internet access at home. Roughly a third (32 percent) have access at work. Almost all of 

the persons with access at work also have access at home, so 56 percent have access 

either at home or at work. Because they are not connected, 45 percent of people spend no 

time on the Internet each week; 10 percent spend an hour or less; 22 percent spend 1 to 5 

hours; 12 percent spend 5 to 10 hours, 7 percent spend 10 to 20 hours, and 5 percent 

spend over 20 hours. If we apply approximate values to each of the intervals8

, we can 

develop a measure of average time spend online each week. Calculated this way, the 
30

average time online is 3.5 hours per week. Among online persons, the average is 6.4 

hours. 

 Table 6 shows how people report using the Internet at work and at home. Some 

of the most popular uses include email (79 percent of home users), shopping (39), and 

news (31).9

 These data are important because they raise the possibility that the Internet 

provides a substitute for other media. If we sum together the number of uses for each 

individual (treating home and work uses separately), the average person (including 

nonusers) reports 3.97 uses, 0.64 of them news-related. 

 Table 8 summarizes the main measures of media use, overall and for news, that 

we employ below to study substitution with individual data. It is worth noting that some 

of these measures are more conventional than others as indicators of media use. For 

broadcast television and the Internet we have time spent with the medium per week. For 

newspapers, by contrast, we have only a binary measure of whether or not a person reads 

a daily paper. For radio and cable (as well as for the Internet) we have the number of 

distinct purposes for which the individual uses the medium. While at first blush an odd 

measure of usage, it is actually a slightly enhanced version of a binary “whether I use” 

measure, where users of the medium who use more than one type of service are coded 

with values above one. (Individuals using N types of Internet sites are coded with N; 

nonusers are coded 0). In the Internet context, where we have both a measure of time 

spent and our unconventional number of uses measure, we can validate the latter. Their 

correlation is 0.65, lending support to the use of this sort of measure. One should bear in 

 8

 We assume persons in the 0-1 category spend 1 hour each week, for the “1-5” group, we assume 2.5; for 

the “5-10” group, we assume 7.5; for the “10-20” group, we assume 15; and for the “20+” group we 

assume 20. 

9

 Another 8 percent go to newspaper sites, and 15 percent “read a newspaper” when online at home. 
31

mind that the virtue of this data source is its inclusion of some measure of individuals’ 

use of a variety of media. 

2. Determinants of Media Use 

 The use of each medium varies across persons with their characteristics, including 

age, education, gender, and race/Hispanic status. Table 7 characterizes the use of the 

media with regressions of various measures of media use on age dummies, education 

dummies, a gender dummy, race/Hispanic status dummies. The excluded age category is 

18-20; the excluded education category is 8th grade or less, and the excluded 

race/Hispanic category is whites. The ten dependent variables are: broadcast TV half 

hours, TV news half hours, number of radio formats used, the number of radio news 

formats used, weekly Internet hours, number of Internet uses, number of Internet news 

uses, whether a person reads a daily newspaper, the number of cable channels used, and 

the number of cable news channels used. 

The use of TV, overall and for news, rises with age and declines with education, 

although the decline with education is slight for news. Use of the radio overall declines 

with age and rises in education, while the use of radio for news in particular rises in both 

age and education. All measures of Internet use fall in age and rise in education. Daily 

newspaper use, like use of radio for news, rises in both age and education. Use of cable 

channels overall declines in age and education, while use of cable news channels rises in 

both age and education. With the exception of TV (and cable overall for blacks), blacks 

and Hispanics use these media less than whites. With the exception of TV, men use these 

media more than women. 
32

III. Substitution 

1. Direct Evidence 

The Scarborough data are unusual in that they allow examination of how users of 

one medium also use other media. That is, they allow researchers to make statements of 

the form, “people who watch a lot of television use the Internet more (or less).” These 

are potentially interesting descriptive facts, and they are suggestive about consumer 

substitution across media. An important caveat is in order, however. It is possible that 

people differ in their interest in information, and this difference may manifest itself in a 

positive correlation among all sorts of media. That is, “media junkies” may read 

newspapers more, watch more television, and use the Internet more. One cannot draw 

firm inferences about substitutability from these data directly without additional 

assumptions. Still, because data of this sort have typically been absent in policy 

discusions, they merit a look before we turn to other attempts to infer substitutability. 

The first approach one might use for studying substitution is examining 

correlation of use measures across media. The correlation of TV viewing and time spent 

on the Internet, for example, reveals whether persons who watch more television also 

spend more time online. Table 9 reports the correlations among our basic measures of 

media use across persons in the Scarborough data. 

Broadcast TV use is negatively correlated with our measures of radio, Internet, 

and cable use. All other correlations in Table 9 are positive. The preponderance of 

positive correlations indicates that persons using more of one medium also use other 
33

media more. In spite of this, the significant negative correlations with TV use suggest 

some avenues of substitution. 

Table 10 reports the correlations of measures of the use of each medium for news. 

All of these correlations, except the Internet-TV relationship, are positive. Again, 

excepting Internet-TV, this indicates that persons who use more news through one 

medium also consume more news though other media. 

Because we have separate measures of news and other (“entertainment”) uses of 

multiple media, we can pose the substitution question a different way. We can calculate 

how much people use each medium for news, relative to their use of the medium for 

other purposes. Term this “relative news use” for each medium. Then we can ask how 

relative news use is correlated across medium. Do people who use television relatively 

more for news use other media relatively less for news? Table 11 reports the correlations 

that answer this question. The estimated correlations are negative and significant for 

TV & Internet, TV & radio, TV & cable, TV & newspapers, and cable & newspapers. 

These negative correlations are consistent with substitution among media. 

A shortcoming of this empirical approach, however, is that it examines only two 

media at a time. In addition, the approach does not account for any of the characteristics 

of the individuals in the sample. To address these concerns, we run three sets of 

regressions that correspond to the three correlation tables above. 

 First, Table 12 reports regressions of individuals’ overall use of each medium on 

their overall use of the other media, as well as a battery of individual characteristics (age, 

gender, race, income). For example, the first column reports a regression with overall 

broadcast TV viewing as the dependent variable and radio, Internet, newspaper, and cable 
34

use as independent variables. The only negative coefficients in the entire table are two 

coefficients reflecting the relationship between television and Internet use. 

 Table 13 performs an exercise analogous to the exercise in Table 12, except that 

the measures are the use of each medium for news rather than overall. We use the same 

measure of newspaper use as above. In this table, as well, the only negative coefficients 

are the TV-Internet coefficients. 

 Table 14 revisits these regressions using the relative use of each medium for 

news. In this table there are a number of negative and significant coefficients, indicating 

that persons making relatively greater use of, say, the Internet for news make relatively 

less use of television for news. The following relationships are negative and significant: 

Internet & TV, newspapers & TV, newspapers & radio, newspapers & Internet, cable & 

newspapers. In all cases, both coefficients corresponding to the relationship in question 

are negative and significant. Putting these results into words, newspapers seem to serve 

as substitutes for TV news on broadcast and cable, radio news, and news on the Internet. 

In addition, the news on the Internet appears to serve as a substitute for news on 

broadcast television. 

2. Indirect Evidence of Substitution 

Existing research documents a strong positive relationship between the size of 

local population(s) and the availability of local media (see Waldfogel, 1999; George and 

Waldfogel, 2000; Waldfogel, 2001). Larger markets have more radio stations, more 

television stations, and more (and more extensive) daily newspapers. Existing studies 
35

document a greater overall tendency for persons to consume each of these local media in 

larger media markets. 

Even stronger than the overall relationship is the relationship between particular 

groups’ tendencies to consume local media and their absolute or relative size in the local 

population. When particular groups of the local population share preferences for media 

content with each other, but not with the remainder of the population, those groups tend 

to listen to more radio, watch more television, and be more likely to purchase newspapers 

in markets where the group in question makes up a larger portion of local population. 

These results suggest that the supply of local media appealing to particular groups 

increase in the size of local group population. We can use this variation to see whether 

the group’s media consumers are more likely to consume nonlocal media when, from 

small local group population, we can infer paucity of local group-targeted media supply. 

Along these lines we propose to examine how use of local and nonlocal media 

among preference minorities vary with the size of their local populations. If nonlocal 

media (the Internet and cable television) are a substitute for local media, then we should 

see: (a) the group tendency to consume local media should increase in the size of the 

local group population (as has been documented elsewhere); and (b) the group tendency 

to consume nonlocal media should decline in the size of the local group population. This 

approach is also employed by Sinai and Waldfogel (2001), which documents this 

relationship for blacks’ Internet use with the Current Population Survey data. 

The next question is what groups to examine. Testing these hypotheses requires 

two conditions. First, preferences must differ across groups. Existing research shows 

that preferences in media products differ sharply by gender, age, race (black vs. non-
36

black) and Hispanic status.10 Second, the relative numbers of persons in the different 

groups must differ across local markets. The percent black and Hispanic vary 

substantially across markets. The percent in each age category and gender vary 

substantially less. Consequently, even if these effects operate by age, they will be 

difficult to detect statistically by age or gender. Our strategy here is to examine these 

relationships along a number of dimensions (age, race, Hispanic status) to see whether we 

can detect the operation of the effects. 

Tables 15-17 examine these relationships by age, race, and Hispanic status. In 

each table we regress our measures of media use on CMSA fixed effects, a group 

dummy, and the group dummy interacted with the CMSA percent in the group, with the 

last variable generating the coefficient of interest. Here we identify the effect from the 

relationship between the group percent in the CMSA and the gap between the group and 

non-group tendencies to use the medium examined in the regression. Essentially, these 

regressions ask whether the gap between, say, young and old persons’ use of each 

medium is larger or smaller in markets with proportionally more young people. For 

media with local content, we expect young persons’ use of media content (relative to 

older persons) to increase in the share of local population that is young. To the extent 

that young persons have different preferences from older persons, we expect their use of 

nonlocal media to be higher in places where young persons make up a small share of 

local population. This would provide evidence, at least indirectly, that nonlocal media 

provide a substitute for local media. 

 

10 See, for example, Waldfogel (1999) for evidence that blacks and whites, and Hispanics and nonHispanics, listen to different radio stations. The formats attracting two thirds of black listening collectively 

attract less than 5 percent of white listening. Waldfogel (2001) documents similar differences between 

black and white preferences in television programming. 
37

Three of the media have an important local component (radio, television, and 

newspapers). Since the supply of content in these media targeted each group grows in the 

size of the local group population, we expect the group use tendency to increase in the 

group’s share of local population. For radio and television, this is what we find. We find 

no relationship for newspapers. For the media that are predominantly nonlocal (Internet 

and cable-or-satellite connection) we expect use to increase in the group’s local isolation. 

This is what we find for both Internet and cable television use. 

What do these estimates mean? A natural interpretation is that the local 

population distribution determines the supply of local media products; hence the positive 

relationships between local group share and the group’s tendencies to consume local 

media. The negative relationships between local group share and the tendency to use 

nonlocal media is naturally interpreted as substitution of nonlocal for local media. 

IV. Concluding Discussion 

 We have now examined consumer substitution among media using a variety of 

data sets and a variety of different approaches. To help sort out the results, we 

summarize them using a table. Essentially, we have examined the relationship among 

the use and numbers of outlets in multiple media. Measures of the following variables 

have appeared at various points in the studies: 

1. TV use overall 

2. TV outlets overall 

3. TV news use 

4. Radio use overall 
38

5. Radio outlets overall 

6. Radio use for news 

7. Radio outlets for news 

8. Daily newspaper use 

9. Daily outlets 

10. Weekly newspaper use 

11. Weekly newspaper outlets 

12. Internet use overall 

13. Internet use for news 

14. Cable use overall 

15. Cable use for news. 

Substitution is reflected by negative relationships among these 15 variables across media. 

Table 18 is a 15 x 15 matrix, partitioned by medium, summarizing the relationships we 

have documented from approaches geared to yield information about substitution. 

 An “A” in the table indicates a negative correlation among changes in aggregate 

measures of outlets or use across media (Table 12 in the aggregate study); “B”, “C,” and 

“D” indicates a negative coefficients from the aggregate changes on changes regressions 

with t-statistics above 1 in absolute value; they are rendered bold if significant at the 5 

percent level (see Tables 13-15 in the aggregate study). An “E” indicates a significant 

negative correlation from Table 9 of this study; “F” and “G” indicate a significant 

negative correlation from Tables10 and 11; finally, “H,” “I,” and “J” indicate significant 

negative coefficients in the regressions in Table 12-14. Note that the correlations are 

treated as symmetric, so that a significant negative correlation results in two table entries 
39

symmetric about the main diagonal. For regression coefficients we treat the column as 

the independent variable and the row as the dependent variable. 

 Standing back, there is clearest evidence of substitution between Internet and 

broadcast TV, both overall and for news; between daily and weekly newspapers; and 

between daily newspapers and TV news. There is also evidence of substitution between 

cable and daily newspapers, both overall and for news consumption; between radio and 

broadcast TV for news consumption; and between the Internet and daily newspapers for 

news consumption. There is little or no evidence of substitution between weekly 

newspapers and TV, or between radio and either Internet or cable. There is also some 

indirect evidence of substitution in the greater use of national media by groups less 

targeted by local media. 

1. What does the Evidence Mean? 

 What do the results in this study say about the questions posed at the outset of the 

study? Do consumers treat different media as substitutes for one another? If there were 

no relationships among use or outlets across media, then one could conclude that there is 

no evidence for substitution. Instead we find negative relationships between a number of 

media. This at least suggests some degree of substitution. 

 Entertainment is an end product in itself, and while some persons (“news 

junkies”) find news entertaining, one might also view the demand for news as a derived 

demand for the information that allows citizens to carry out their lives. Weather 

information helps parents dress their children – and themselves – appropriately in the 
40

morning. Political information makes it easier for citizens to know what is at stake in 

electoral contests and, in turn, can make them more likely to vote. 

 If substitution is complete, then the decline of local daily newspapers will be 

offset by increased use of other media. We can define another sense in which 

substitution can be complete or incomplete. With complete substitution, the civic 

behaviors affected by media consumption will also be unaffected by changes in 

availability or use of any particular medium. One might term this the “behavioral 

neutrality” implication of complete substitution. 

Existing research on media consumption and voting suggests that, even if 

substitution operates, it is not complete in the latter sense. Changes in availability of 

black-targeted radio stations and weekly newspapers, 1994-1998, affect the tendency for 

blacks to vote (see Oberholzer-Gee and Waldfogel, 2001). There would be no effect on 

voting if changes in use of other media offset the effects of changes in availability of 

black-targeted radio stations and weeklies. Although there is evidence (above, and Sinai 

and Waldfogel, 2001) that small groups substitute nonlocal media (such as cable and 

Internet) for local media where local products are unavailable, this substitution does not 

generate behavioral neutrality. 

In another context there is additional evidence against behavioral neutrality. 

National newspapers serve as substitutes for local ones, in the sense that markets with 

faster growth in a major national paper’s circulation experience greater decline in local 

daily paper circulation among readers targeted by the national paper (see George and 

Waldfogel, 2002). Behavioral neutrality fails in the sense that in markets with greater 

increase in the national paper’s circulation, there is a greater decline in the tendency for 
41

the targeted audience to vote in local (non-presidential) elections. Here again, although 

consumers view the products are substitutes, they do not have the same effect on civic 

behavior. 

The conception of each medium as entirely distinct would be unduly restrictive 

because there is evidence (here and elsewhere) that consumers substitute across media. 

At the same time, however, substitution is not apparently so complete that the effects of 

changes in one medium are offset by changes in another to leave civic behavior 

unchanged. It is conventional and trite to conclude a study with a call for more research. 

Nonetheless, some of these questions will only be answered with additional research. 
42

References 

BIA, BIA Media Access Pro – TV, 2001. http://www.bia.com/mediapro.asp 

Berry Steven T. and Joel Waldfogel. "Free Entry and Social Inefficiency in Radio 

Broadcasting." RAND Journal of Economics 30, 3 (1999a):397-420. 

Berry Steven T. and Joel Waldfogel. "Public Radio in the United States: Does it Correct 

Market Failure or Cannibalize Commercial Stations?" Journal of Public Economics 71 

(1999b): 189-211. 

Berry Steven T. and Joel Waldfogel. “Mergers, Station Entry, and Programming Variety 

in Radio Broadcasting.” Quarterly Journal of Economics, 2001. 

Burrelle’s Media Services. Burrelle’s Media Directory, 1993 and 1999. 

http://www.burrelles.com/indexmd.html 

Downes, Tom; Greenstein, Shane. “Universal Access and Local Internet Markets in the 

US.” Research Policy. Vol. 31 (7). p 1035-52. September 2002. 

Duncan, James. Duncan’s American Radio, Spring 1993 and Spring 1997 Issues. 

http://www.duncanradio.com/AmericanRadio.html 

Lisa George and Joel Waldfogel. “Who Benefits Whom in Daily Newspaper Markets? 

NBER Working Paper 7944, October 2000. Accessible at 

http://rider.wharton.upenn.edu/~waldfogj/workpap.html 

Lisa George and Joel Waldfogel. “Does the New York Times Spread Ignorance and 

Apathy?” The Wharton School, revised June 2002. Accessible at 

http://rider.wharton.upenn.edu/~waldfogj/workpap.html 

Felix Oberholzer Gee and Joel Waldfogel. “Electoral Acceleration: The Effect of 

Minority Population on Minority Voter Turnout”, NBER Working Paper 8252, April 

2001. Accessible at http://rider.wharton.upenn.edu/~waldfogj/workpap.html 

Scarborough Research, Primenext Multimarket Data, 1999-2000. 

http://www.scarborough.com/prime/index.html 

Peter Siegelman and Joel Waldfogel. “Race and Radio: Preference Externalities, 

Minority Ownership, and the Provision of Programming to Minorities.” Advances in 

Applied Microeconomics, volume 10, 2001. 

Todd Sinai and Joel Waldfogel. “Geography and the Internet: Is the Internet a Substitute 

or a Complement for Cities?” Mimeo, The Wharton School 

Accessible at http://rider.wharton.upenn.edu/~waldfogj/workpap.html 
43

U.S. Department of Commerce. Current Population Surveys, (Computer and Internet Use 

Supplements 1997, 1998, 2000). http://www.bls.census.gov/cps/cpsmain.htm 

Joel Waldfogel. “Who Benefits Whom in Local Television Markets?”, mimeo, The 

Wharton School, December 2001. Accessible at 

http://rider.wharton.upenn.edu/~waldfogj/workpap.html 

Joel Waldfogel. “Preference Externalities: An Empirical Study of Who Benefits Whom 

in Differentiated Product Markets” NBER Working Paper 7391, October 1999 

Accessible at http://rider.wharton.upenn.edu/~waldfogj/workpap.html 
44

TABLES (AND FIGURE) FOR PART I 
45

Figure 1: Media Outlets and Market Size 

Pop Total 1995

 Broadcast TV Stations 2000 Daily Papers '99 Weekly Papers '99 Stations Heard in Metro - 1997

177 18844

1

824

Notes: Log scales for population on the horizontal axis and the number of outlets in each medium on the vertical explanation. 
46

Table 1: Television Outlets, 1994-2000 

year N uhf vhf uhf+vhf network indep. public net+ind+pub 

1994 207 2.29 2.63 2.46 1995 207 2.96 2.29 5.25 2.72 2.52 1.71 6.96 1996 207 3.09 2.34 5.43 4.20 1.23 1.71 7.14 1997 207 3.19 2.35 5.54 4.29 1.25 1.71 7.25 1998 207 3.37 2.37 5.74 4.74 1.40 1.72 7.87 1999 207 3.54 2.37 5.91 4.94 1.46 2000 207 3.65 2.39 6.04 5.02 1.57 1.73 8.32 

 linked sample year N uhf vhf uhf+vhf network indep. public net+ind+pub 

1994 2.64 2.88 3.33 1995 143 3.76 2.63 6.38 3.01 3.37 2.04 8.42 1996 143 3.88 2.68 6.56 4.85 1.71 2.03 8.59 1997 143 3.98 2.69 6.67 4.95 1.72 2.03 8.71 1998 143 4.19 2.71 6.90 5.26 1.90 2.05 9.21 1999 143 4.40 2.71 7.11 5.43 1.99 2000 143 4.55 2.73 7.28 5.52 2.13 2.07 9.72 
47

Table 2: Cable and Television Use, 1994-2000 

 All Markets Linked Sample 

Year N hut % cable % N hut % cable % 

1994 207 36.47 63.19 143 37.29 62.96 

1995 207 36.41 63.88 143 37.18 63.93 

1996 207 36.69 66.30 143 37.47 66.41 

1997 207 36.04 67.05 143 36.85 67.21 

1998 207 35.88 67.28 143 36.82 67.53 

1999 207 35.66 66.90 143 36.86 67.43 

2000 207 35.47 67.08 143 36.80 67.76 
48

Table 3: Quarterly Use of Television, Overall and News, 1999-2001 

 hut 

date total day prime time Eve. News Nightly News 

Nov-99 207 36.11 56.41 50.44 41.79

Feb-00 207 36.67 57.57 52.24 43.55

May-00 207 33.74 54.44 46.36 43.33

Jul-00 207 33.42 48.60 44.97 42.07

Nov-00 207 37.55 57.06 51.81 43.31

Feb-01 207 37.22 57.58 52.89 43.52

May-01 207 33.66 54.07 46.03 43.39

Jul-01 207 33.67 48.47 44.79 42.38

 linked sample 

date total day prime time Eve. News Nightly News 

Nov-99 143 37.46 57.81 51.36 43.31

Feb-00 143 37.85 58.81 52.92 44.80

May-00 143 35.07 55.98 47.22 44.90

Jul-00 143 34.83 50.21 45.79 43.43

Nov-00 143 38.95 58.47 52.76 44.78

Feb-01 143 38.53 58.98 53.69 44.87

May-01 143 35.15 55.80 47.09 44.94

Jul-01 143 35.06 50.03 45.77 43.72

 

 total share 

 total day prime time Eve. News Nightly News 

Nov-99 207 56.72 60.36 65.49 68.57

Feb-00 207 55.87 61.14 65.50 69.01

May-00 207 54.76 60.21 63.46 66.68

Jul-00 207 48.92 62.60 61.60

Nov-00 207 52.40 57.12 61.35 64.89

Feb-01 207 52.88 58.27 62.78 66.00

May-01 207 52.30 57.15 60.76 64.00

Jul-01 207 46.48 46.06 59.65 

 

 Linked sample 

date total day prime time Eve. News Nightly News 

Nov-99 143 60.10 64.54 68.30 70.81

Feb-00 143 59.14 65.36 68.19 70.97

May-00 143 57.96 64.25 65.97 68.84

Jul-00 143 51.71 64.34 63.11

Nov-00 143 55.78 61.30 63.90 66.82

Feb-01 143 56.22 62.51 65.35 68.20

May-01 143 55.45 61.21 63.15 66.17

Jul-01 143 49.06 49.57 61.41 
49

Table 4: Radio Formats, 1993 & 1997

 1993 1997 

N % N % 

Adult Contemp (AC) 462 8.44 AC 418 6.62 

AC/Album Oriented Rock (AOR) 8 0.15 AC/AOR 4 0.06 

AC/ Contemp. Hit Radio (CHR) 10 0.18 AC/CHR 128 2.03 

AC/Full Service (FS) 1 0.02 AC/NR 42 0.67 

AC/Oldies (O) 5 0.09 AC/SAC 8 0.13 

AOR 565 10.32 AOR 375 5.94 

AOR/AC 5 0.09 AOR/AC 3 0.05 

Black (B) 281 5.13 AOR/NR 200 3.17 

B/AC 13 0.24 AOR/P 80 1.27 

B/Gospel (G) 4 0.07 B 236 3.74 

B/O 3 0.05 B/AC 107 1.69 

B/Talk (T) 4 0.07 B/G 32 0.51 

Big Band/Nostalgia (BB) 213 3.89 B/O 26 0.41 

BB/EZ 7 0.13 B/T 6 0.1 

BB/FS 5 0.09 BB 315 4.99 

BB/O 1 0.02 BB/EZ 3 0.05 

Country (C) 772 14.1 BB/REL 1 0.02 

C/FS 20 0.37 BB/T 1 0.02 

CHR 488 8.91 C 904 14.32

CHR/AC 15 0.27 C/FS 14 0.22 

CHR/Spanish (SP) 3 0.05 CHR 410 6.49 

CHR/Urban (U) 9 0.16 CHR/AC 51 0.81 

Classical (CL) 67 1.22 CHR/NR 9 0.14 

Classic AOR(CL AOR) 151 2.76 CHR/U 56 0.89 

Ethnic (E) 16 0.29 CL 70 1.11 

Easy Listening (EZ) 2 0.04 CL AOR 284 4.5 

EZ/New Age Contemp. (NAC) 1 0.02 CL HITS 83 1.31 

EZ/SAC 3 0.05 E 14 0.22 

FS 202 3.69 EZ 13 0.21 

FS/T 41 0.75 EZ/SAC 1 0.02 

G 55 1 FS 96 1.52 

G/B 1 0.02 FS/T 81 1.28 

Jazz (J) 59 1.08 G 62 0.98 

News (N) 37 0.68 J 118 1.87 

News/Talk (N/T) 161 2.94 KIDS 1 0.02 

NAC/AOR 2 0.04 N 75 1.19 

O 352 6.43 N/T 170 2.69 

Religious (REL) 200 3.65 O 394 6.24 

Soft Adult Contemp. (SAC) 379 6.92 REL 276 4.37 

SP 172 3.14 SAC 253 4.01 

Sports (SPRTS) 70 1.28 SP 296 4.69 

Talk 247 4.51 SPRTS 172 2.72 

T/O 4 0.07 T 408 6.46 

Unknown 360 6.57 T/CL AOR 7 0.11 

 T/J 5 0.08 

 UNK 6 0.1 

 
50

Total 5476 100 6314 100 
51

Table 5: Radio Outlets and Listening

 Mean Stations News Stations 

Year N AQH List. Inside All Inside All Formats 

1993 230 16.83 15.00 21.73 0.46 0.79 11.60 

1994 230 16.48 

1995 230 16.28 

1996 230 16.07 

1997 230 15.83 16.65 24.83 0.55 0.94 14.97 

1998 230 15.65 

 

 Linked Sample 

 Mean Stations News Stations 

 N AQH List. Inside All Inside All Formats 

1993 142 16.77 22.76 27.83 0.76 1.01 

1994 137 16.57 

1995 139 16.33 

1996 139 16.13 

1997 143 15.78 24.96 31.43 0.88 1.20 

1998 139 15.74 

 

Note: N=143 for all but AQH 
52

Table 6: Newspaper Outlets and Circulation, 1993-1999 

 Daily Daily Weekly Weekly 

Year N # Outlets Circulation # Outlets Circulation 

1993 210 8.05 309580 44.18 531694 

1999 210 7.85 295950 46.21 728804 

 

 Linked Sample 

 Daily Daily Weekly Weekly 

Year N # Outlets Circulation # Outlets Circulation 

1993 143 10.15 427775 56.98 737695 

1999 143 9.92 409149 60.20 1025342 
53

Table 7: Trends in Internet Use, 1997-2000 

 Full Sample 

 connection Use for Use for Use for 

Year home work mail news info 

1997 15.14% 11.95% 7.08% 12.08%

1998 30.25% 19.82% 12.04% 15.73%

 

2000 45.90% 33.62% 19.59% 23.81%

 Linked Sample 

 connection Use for Use for Use for 

Year home work mail news info 

1997 143 15.64% 12.05% 12.52% 7.47% 12.36%

1998 143 30.94% 14.13% 20.80% 13.19% 16.89%

 

2000 143 46.56% 17.23% 34.65% 20.92% 25.39%
54

Table 8: Medium Use and Outlets, Cross Sectional Evidence  (1) (2) (3) (4) (5) (6)  TV 

HUT % '97 

Radio 

AQH List. '97 

Weekly 

Newspaper 

Circ/Pop. '99 

Daily 

Newspaper 

Circ/Pop. '99 

TV 

HUT % '97 

Radio 

AQH List. '97 

W

Ne

Circ

TV Stations # uhf & vhf stations '97 

0.7923 1.0346 

 (0.1052)** (0.1278)** Radio Stations # Rec'd '97 

 0.0199 0.0374 

 (0.0049)** (0.0083)** Newspapers # Weeklies '99 

 0.0827 0

 (0.0357)* (0Newspaper # Dailies '99 

 0.4121 

 (0.0551)** Constant 31.5121 15.2567 31.9608 16.1550 29.8973 14.7054 2 (0.7706)** (0.1751)** (3.7091)** (0.7280)** (0.9121)** (0.2742)** (4.Observations 141 141 141 141 141 141 Method OLS OLS OLS OLS IV IV R-squared 0.29 0.11 0.04 0.29 0.26 0.02 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1%. IV regressions use log population as the instrument for 
55

Table 9: Medium Use and Market Size  (1) (2) (3) (4) (5) (6)  HUT % '97 AQH List. '97 % Int at home 

'98 

Weekly Circ/Pop. '99 

Daily Circ/Pop. '99 

% Cable '97 

1995 Pop. (000) 0.9505 0.1701 0.3851 2.6237 0.8302 -0.1345  (0.1418)** (0.0358)** (0.3252) (1.3116)* (0.2336)** (0.2774) Constant 35.1828 15.5939 30.2242 32.4655 18.8534 67.3699  (0.4075)** (0.1027)** (0.9343)** (3.7686)** (0.6713)** (0.7971)** Observations 141 141 141 141 141 141 R-squared 0.24 0.14 0.01 0.03 0.08 0.00 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1% 
56

Table 10: Outlets and Media Use: Fixed Effects Regressions  (1) (2) (3) (4) (5)  HUT AQH List. AQH News List. Daily Circ. pc Weekly Circ. pc TV Stations 0.3133  (0.1365)* Radio Stations -0.0156  (0.0157) Radio News Stations 0.2412  (0.0369)** # Weeklies 0.0518  (0.0364) # Dailes 1.2074  (0.2201)** Year=1995 0.6581  (0.2640)* Year=1996 0.8933  (0.2537)** Year=1997 0.2337 -0.8340 -0.2167  (0.2481) (0.1010)** (0.0373)** Year=1998 0.1435  (0.2397) Year=1999 0.1172 -1.2545 0.0904  (0.2348) (0.2608)** (0.7614) Constant 34.4717 17.2034 0.3859 9.4843 33.7189  (1.0068)** (0.4437)** (0.0451)** (2.2506)** (2.1361)** Observations 846 281 281 282 282 Number of DMA -- Nielsen 141 141 141 141 141 R-squared 0.03 0.45 0.32 0.32 0.02 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1% 
57

TABLE 11: Correlations among Cross Sectional Measures of Outlets and Use of Various Media 

| TV ‘99 

Radio 

(In) ’97 

Radio 

News 

(In) ‘97 

Dailies 

‘99 

Weeklies 

‘99 TV ‘98

Radio 

‘97 

Radio 

News ‘97 

Radio 

News 

In ‘97 

Dailies 

‘99 

Weeklies 

‘99 

Int

‘

Outlets:TV ‘99 1 Radio (In) ‘97 0.7555* 1 Radio News (In) ‘97 0.4699* 0.5618* 1 Dailies ‘99 0.6505* 0.6661* 0.2919* 1 Weeklies ‘99 0.5342* 0.6449* 0.3391* 0.8096* 1 Use:

TV ‘98 0.5366* 0.3936* 0.2521* 0.3909* 0.2921* 1 Radio ‘97 0.3569* 0.3395* 0.3175* 0.2356* 0.3144* 0.3338* 1 Radio News ‘97 0.2511* 0.2489* 0.7148* 0.2152* 0.2553* 0.2091* 0.3348* 1 Radio News In ‘97 0.2459* 0.2091* 0.7001* 0.1949* 0.2173* 0.2043* 0.3167* 0.9870* 1 Dailies ‘99 0.2080* 0.5356* 0.3669* 1 Weeklies ‘99 0.1573 0.1959* 0.1925* 0.1511 0.1428 1 Internet ‘98 0.1415 Internet News ‘98 0.1619 0.1659* 0.7Cable ‘98 0.2266* 0.1543 0.2354* 0.1946* 

Note: Correlation reported if significant at 10% level; star indicates 5% significance level. 
58

TABLE 12: Correlations among Changes in Measures of Outlets and Use of Various Media 

∆outlets ∆outlets ∆outlets ∆outlets ∆outlets ∆use ∆use ∆use ∆use ∆use ∆us

 TV Radio Radio News

Daily 

Papers 

Weekly 

Papers TV Radio

Radio 

News 

Radio 

News 

(Inside) 

Daily 

Papers 

Week

Pape

Change in Outlets:TV 1 Radio 0.1407 1 Radio News 0.2774* 1 Daily Papers 1 Weekly Papers -0.4411* 1 Change in Use:

TV 1 Radio 1 Radio News 0.5306* 1 Radio News (Inside) 0.5470* 0.9610* 1 Daily Papers 0.4218* 1 Weekly Papers 1 Internet -0.1782* Internet News -0.1638 Cable -0.2319* -0.1727* 0.1559 0.2025* -0.1654* 

Note: Correlation reported if significant at 10% level; star indicates 5% significance level. 
59

Table 13: The Relationship among Changes in Media Outlets  (1) (2) (3) (4)  Chg. TV Stns. '94-

'00 

Chg. Daily Papers '93-

'99 

Chg. Weekly Papers 

'93-'99 

Chg. Inside Radio Stns 

'93-'97 

Chg. Inside Radio Stns '93-'97 0.0346 0.0211 -0.1113  (1.52) (0.95) (0.28) Chg. Daily Papers '93-'99 0.1354 -7.9469 0.3099  (1.55) (5.76)** (0.95) Chg. Weekly Papers '93-'99 0.0038 -0.0245 -0.0050  (0.78) (5.76)** (0.28) Chg. TV Stns. '94-'00 0.1276 1.1592 0.4787  (1.55) (0.78) (1.52) Constant 0.8435 -0.3134 0.4616 1.8602  (7.93)** (2.57)* (0.21) (4.13)** H0: Cross effects=0 (p-val) (0.16) (0.00) (0.00) (0.23) 

Observations 141 141 141 141 R-squared 0.04 0.22 0.20 0.03 

Notes: Absolute value of t statistics in parentheses. * significant at 5%; ** significant at 1% 
60

Table 14: Changes in Use and Change in Outlets  (1) (2) (3) (4) (5) (6)  Chg. HUT 

Viewing '95-

'00 

Chg. Daily 

Paper Circ. 

per cap '93-

'99 

Chg. Weekly 

Paper Circ. 

per cap '93-

'99 

Chg. AQH 

List '94-'98 

Chg. AQH 

News List 

'93-'97 

Chg. Cable Use '94-'00 

In

Chg. TV Stns. '94-'00 0.6104 -0.0157 0.1023 0.0779 -0.0259 -0.3002  (1.84) (0.07) (0.14) (1.33) (0.78) (1.06) Chg. Inside Radio Stns '93-'97 -0.0898 -0.1078 -0.1958 0.0091 -0.1842  (1.01) (1.67) (1.02) (0.56) (2.43)* Chg. Daily Papers '93-'99 -0.2157 1.4125 -0.1983 -0.0929 0.0028 -0.2911  (0.63) (5.72)** (0.27) (1.56) (0.08) (1.00) Chg. Weekly Papers '93-'99 0.0075 0.0203 0.0444 -0.0033 0.0024 0.0180  (0.39) (1.48) (1.09) (1.01) (1.24) (1.11) Chg. Inside News Radio Stns '93-'97 

 0.3252 

 (7.25)** Constant -0.9343 -1.0182 0.4078 -0.9183 -0.1944 5.3499  (1.88) (2.83)** (0.38) (10.05)** (4.10)** (12.62)** (H0: Cross effects=0 (p-val) (0.53) (0.17) (0.76) (0.28) (0.50) (0.01) 

Observations 141 141 141 135 140 141 R-squared 0.03 0.21 0.02 0.03 0.29 0.09 

Notes: Absolute value of t statistics in parentheses. * significant at 5%; ** significant at 1% 
61

Table 15: Changes in Use and Changes in Use  (1) (2) (3) (4) (5) (6)  Chg. HUT 

Viewing '95-

'00 

Chg. Daily 

Paper Circ. 

per cap '93-

'99 

Chg. Weekly 

Paper Circ. 

per cap '93-

'99 

Chg. AQH 

List '94-'98 

Chg. AQH 

News List 

'93-'97 

Chg. Cable Use '94-'00 

In

Chg. AQH List '94-'98 0.3922 0.1176 -0.7387 -0.2400  (0.78) (0.29) (0.68) (0.61) Chg. Daily Paper Circ. per cap '93-'99 

0.0377 0.1188 0.0054 0.0065 -0.1676 

 (0.35) (0.51) (0.29) (0.51) (2.01)* Chg. Weekly Paper Circ. per cap '93-'99 

0.0083 0.0166 -0.0048 0.0010 -0.0376 

 (0.21) (0.51) (0.68) (0.21) (1.19) Chg. Cable Use '94-'00 0.2905 -0.1809 -0.2903 -0.0119 -0.0017  (2.66)** (2.01)* (1.19) (0.61) (0.13) Int Use '00 0.0126 0.0030 0.1042 -0.0045 0.0276  (0.35) (0.10) (1.33) (0.72) (0.98) Chg. HUT Viewing '95-'00 0.0251 0.0396 0.0121 -0.0117 0.1794  (0.35) (0.21) (0.78) (1.14) (2.66)** Int News Use '00 -0.0073  (1.01) Chg. AQH Inside News List '93-'97 

 

 Constant -2.0030 -0.7021 -3.6352 -0.5326 -0.0025 3.1950 4 (1.14) (0.49) (0.94) (1.74) (0.02) (2.35)* (2H0: Cross effects=0 (p-val) (0.17) (0.45) (0.55) (0.85) (0.75) (0.02) 

Observations 135 135 135 135 140 135 R-squared 0.07 0.04 0.03 0.02 0.03 0.10 

Notes: Absolute value of t statistics in parentheses. * significant at 5%; ** significant at 1% 
62

TABLES FOR PART II 
63

Table 1: Newspaper Use 

 Read a Daily? 

No Yes 

Business pages/section 29.00% 62.23%

Classified Advertising 36.72% 55.76%

Comics 33.12% 60.88%

Entertainment 41.52% 68.73%

Food 29.05% 58.77%

News 55.19% 90.77%

Sports 31.61% 60.77%

TV or Radio Listings 27.42% 55.22%

 

N 83224 95560 

Table 2: Cable and Satellite 

 percent 

Basic 22.76% 

Basic/Extended plus Pay 21.93% 

None 26.71% 

Extended 28.59% 

 

N 178784 

 

 percent 

No Satellite 86.13% 

Have Satellite 13.87% 

DirectTV 6.35% 

Dish 3.02% 

Other Provider 3.66% 

Primestar 0.91% 

USSB 0.47% 

 

N 178784 
64

Table 3: Radio Use 

Number of Formats Listened to percent 

0 11.39%

1 19.83%

2 27.68%

3 21.15%

4 11.44%

5 5.25%

6 2.10%

7 0.76%

8 0.30%

9 0.09%

10 0.02%

 

 178784
65

Table 4: Radio Format Use 

Format 

percent 

listening 

to format 

Adult Contemporary 18.29%

Other 0.25%

Adult Standards 4.44%

Album Adult Alternative 2.52%

Album Oriented Rock 11.81%

All News 

5.35%

All Sports 5.19%

Alternative 7.25%

Black 0.26%

Business 

0.00%

Childrens Radio (Rel.2 Only) 0.40%

Classic Country 0.79%

Classic Rock 11.08%

Classical 7.62%

Contemporary Christian 2.81%

Contemporary Hit Radio 14.41%

Pop Contemporary Hit Radio 6.03%

Country 21.22%

Rhythmic Contemporary Hit Radio (Rel. 2 Only) 1.74%

Dance 0.00%

Easy Listening 0.72%

Educational (Rel.2 Only) 0.86%

Ethnic 0.39%

Gospel 1.40%

Hot AC 9.67%

Modern AC 3.69%

Jazz 1.68%

New AC/Smooth Jazz 4.86%

New Age 0.00%

New Country 1.43%

New Rock 2.17%

News/Talk/Information 

23.02%

Nostalgia 1.40%

70's Rock 0.01%

70's Oldies/Classic Hits 2.72%

Oldies 15.26%

Religious 5.97%

Rhythm & Blues 0.38%

Rhythmic Oldies 3.27%

Soft Adult Contemporary 5.81%

Spanish News/Talk (Rel.2 Only) 0.70%

Spanish Contemporary 2.44%
66

Spanish Regional 2.17%

Spanish Language 0.50%

Spanish Tropical 0.91%

Southern Gospel 0.50%

Talk/Personality 3.91%

Urban AC 3.48%

Tejano 0.33%

Urban Contemporary 6.18%

Urban Oldies 0.67%

Variety 3.67%

Spanish label var viety 0.28%

Format Not Provided 0.00%

Notes: News-related formats underlined in table. 
67

Table 5: Internet Use 

hours/week percent 

0 45.15%

0-1 9.93%

1-4 21.76%

5-9 11.59%

10-19 6.59%

20+ 4.98%

 

None 43.96%

Home Only 23.82%

Work Only 1.18%

Both 31.03%

 

N 178784
68

Table 6: Internet Uses among Home and Work Users 

 

use 

% using at

home for...

% using at

work for...

Auction site 11.6% 4.5% 

Automobile information 19.5% 8.8% 

Cable network site 2.9% 1.7% 

Chat or community site 16.8% 5.8% 

Research/education 79.1% 84.2% 

E-mail 24.5% 20.9% 

Financial information/services 28.6% 9.2% 

Games 17.0% 13.2% 

Job/employment search 9.8% 6.7% 

Local cable system site 1.2% 0.5% 

Local information 

22.1% 15.2% 

Local television station site 3.7% 2.0% 

Magazine site 7.0% 5.6% 

Medical services/information 6.3% 5.3% 

News 

18.5% 10.0% 

Newspaper site 

31.0% 31.0% 

Other use 7.6% 7.1% 

Other newspaper site 

1.0% 1.1% 

Maintain own home page 17.1% 13.3% 

Purchase item or service 21.4% 12.0% 

Listen to audio programming/radio station 7.6% 5.3% 

Radio station site 5.4% 4.2% 

Real estate listings 14.6% 13.1% 

Read a magazine 12.0% 5.7% 

Read a newspaper 

37.7% 37.3% 

Shopping 39.0% 18.6% 

Sports scores/updates 18.4% 12.4% 

Television network site 5.7% 3.3% 

Watch video programming 1.7% 1.0% 

Work-related use 12.5% 49.2% 

 

Users 98067 57592 

Notes: News-related uses underlined. 
69

Table 7: Who Uses Each Medium?  (1) (2) (3) (4) (5) (6) (7) (8)  TV Half 

Hours per 

Week 

TV News 

Half Hours 

(4-12pm) 

# Radio 

Formats 

Used 

# Radio 

News 

Formats 

Used 

Weekly Internet 

Hours 

# Internet 

Uses 

# Internet 

News Uses 

Daily 

Newspaper 

age 21-20 0.8925 0.0363 -0.2160 -0.0206 -0.2466 -0.2701 0.0258 -0.0663  (0.4452)* (0.0882) (0.0211)** (0.0069)** (0.0787)** (0.0725)** (0.0180) (0.0070)** age 25-29 2.0167 0.2560 -0.2765 0.0145 -0.4463 -0.4199 0.0157 -0.0740  (0.4102)** (0.0813)** (0.0194)** (0.0063)* (0.0725)** (0.0668)** (0.0166) (0.0065)** age 30-34 2.1558 0.3457 -0.2838 0.0688 -0.6439 -0.6339 -0.0258 -0.0289  (0.4020)** (0.0797)** (0.0190)** (0.0062)** (0.0710)** (0.0654)** (0.0162) (0.0063)** age 35-39 3.7367 0.8860 -0.3352 0.1219 -1.1255 -1.0243 -0.0870 0.0367  (0.3954)** (0.0784)** (0.0187)** (0.0061)** (0.0699)** (0.0644)** (0.0160)** (0.0062)** age 40-44 4.3695 1.1062 -0.4043 0.1545 -1.1787 -1.2470 -0.1260 0.0990  (0.3979)** (0.0789)** (0.0188)** (0.0061)** (0.0703)** (0.0648)** (0.0161)** (0.0063)** age 45-49 5.7908 1.4608 -0.5088 0.1886 -1.1845 -1.3372 -0.1535 0.1343  (0.4081)** (0.0809)** (0.0193)** (0.0063)** (0.0721)** (0.0664)** (0.0165)** (0.0064)** age 50-54 11.6471 3.1224 -0.7699 0.2179 -1.7841 -2.4368 -0.3524 0.1679  (0.4139)** (0.0820)** (0.0196)** (0.0064)** (0.0731)** (0.0674)** (0.0167)** (0.0065)** age 55-59 14.4908 3.8996 -1.0045 0.2675 -2.0782 -2.9276 -0.4371 0.2074  (0.4367)** (0.0866)** (0.0207)** (0.0067)** (0.0772)** (0.0711)** (0.0176)** (0.0069)** age 60-64 18.0567 4.5624 -1.1911 0.2889 -2.5092 -3.5055 -0.5042 0.2502  (0.4539)** (0.0900)** (0.0215)** (0.0070)** (0.0802)** (0.0739)** (0.0183)** (0.0072)** age 65-69 22.3543 5.5710 -1.4182 0.3061 -2.9543 -4.0423 -0.5726 0.2947  (0.4623)** (0.0916)** (0.0219)** (0.0071)** (0.0817)** (0.0752)** (0.0187)** (0.0073)** age 70+ 26.5446 6.5158 -1.7677 0.2882 -3.5353 -4.5105 -0.6388 0.3131  (0.3943)** (0.0782)** (0.0187)** (0.0061)** (0.0697)** (0.0642)** (0.0159)** (0.0062)** some high 2.4028 0.5135 0.3248 0.0450 0.0443 -0.1540 -0.0280 0.1312  (0.4605)** (0.0913)** (0.0218)** (0.0071)** (0.0814) (0.0750)* (0.0186) (0.0073)** high 0.7760 0.4254 0.5415 0.1066 0.4578 0.1872 0.0214 0.2388  (0.3919)* (0.0777)** (0.0186)** (0.0060)** (0.0692)** (0.0638)** (0.0158) (0.0062)** some coll -1.2629 0.1224 0.7507 0.1759 1.4572 1.4282 0.2349 0.2952  (0.4052)** (0.0803) (0.0192)** (0.0062)** (0.0716)** (0.0659)** (0.0164)** (0.0064)** college -2.7839 -0.0888 0.8960 0.2711 1.9110 2.2537 0.4021 0.3429  (0.4129)** (0.0818) (0.0195)** (0.0064)** (0.0730)** (0.0672)** (0.0167)** (0.0065)** some grad -3.5798 -0.2026 1.0047 0.3400 2.3446 2.9443 0.5184 0.3710  (0.5137)** (0.1018)* (0.0243)** (0.0079)** (0.0908)** (0.0836)** (0.0207)** (0.0081)** grad schl -5.1676 -0.3416 0.9257 0.3576 2.5269 3.1556 0.5609 0.3936  (0.4345)** (0.0861)** (0.0206)** (0.0067)** (0.0768)** (0.0707)** (0.0175)** (0.0069)** Black 9.9758 0.7268 -0.2975 -0.1157 -0.7968 -0.8268 -0.1161 -0.0329 
70

 (0.2393)** (0.0474)** (0.0113)** (0.0037)** (0.0423)** (0.0389)** (0.0097)** (0.0038)** Asian -1.5797 -0.2753 -0.7174 -0.0073 1.0704 1.0196 0.2998 -0.0705  (0.5068)** (0.1004)** (0.0240)** (0.0078) (0.0895)** (0.0825)** (0.0205)** (0.0080)** Oth Non-Hisp 0.2576 -0.0468 -0.3040 -0.0452 -0.0221 0.0079 0.0094 -0.0597  (0.4750) (0.0941) (0.0225)** (0.0073)** (0.0839) (0.0773) (0.0192) (0.0075)** Hisp 5.5291 1.1266 -0.1834 -0.0862 -1.1152 -1.1895 -0.1600 -0.0846  (0.2541)** (0.0504)** (0.0120)** (0.0039)** (0.0449)** (0.0414)** (0.0103)** (0.0040)** Male -6.6350 -0.0570 0.1505 0.0972 1.0631 1.2149 0.2628 0.0656  (0.1445)** (0.0286)* (0.0068)** (0.0022)** (0.0255)** (0.0235)** (0.0058)** (0.0023)** Constant 28.6164 2.7419 2.3282 -0.0764 3.5133 4.2392 0.5687 0.1356  (0.5149)** (0.1021)** (0.0244)** (0.0079)** (0.0910)** (0.0838)** (0.0208)** (0.0081)** Observations 178784 178784 178784 178784 178784 178784 178784 178784 R-squared 0.11 0.12 0.16 0.11 0.08 0.16 0.09 0.10 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1%.
71

Table 8: Sample Statistics 

mean 

TV Half Hours/Week 35.47News Half Hours (4-12 PM) 5.31

Whether Listen to Radio 88.61%# Radio Formats Listened to 2.32# Radio News Formats Used 0.28Whether Use Internet 54.85%# Internet Uses 3.97# Internet News Uses 0.64Weekly Time Online 3.50Whether Cable or Satellite 79.81%# Cable Channels Used 8.40# Cable News Channels Used 0.82Whether Daily Newspaper 53.45%
72

Table 9: Correlations among Overall Use Measures 

shortname Variable view radall dradio itime iuses dint cab_num dcabsat any_dview Television Half Hours per week 1 radall Radio Formats Listened to -0.1149 1  0 dradio Whether Listen to Radio -0.0937 0.5387 1  0 0 itime Weekly Time Online -0.1044 0.117 0.0808 1  0 0 0 iuses Number of Internet Uses -0.144 0.1787 0.1148 0.6536 1  0 0 0 0 dint Whether online -0.1785 0.2015 0.1516 0.5745 0.6533 1  0 0 0 0 0 cab_num Number of Cable Channels Used -0.015 0.0712 0.0465 0.1196 0.1539 0.0933 1  0 0 0 0 0 0 dcabsat Whether Use Cable or Satellite -0.0684 0.0088 0.0121 0.0584 0.057 0.0806 0.4788 1  0 0.0002 0 0 0 0 0 any_daily Whether Read a Daily 0.0536 0.0299 0.0208 0.0022 0.003 0.0066 0.0393 0.0712 1  0 0 0 0.3458 0.2071 0.0052 0 0 

Note: probability values appear below correlations in table. 
73

Table 10: Correlations among News Use Measures 

 TVnews inews_~s rnews cab_news any_da~y TVnews TV News Half Hours (4-12PM) 1 inews_uses Number of Internet News Uses -0.0909 1 

 0 rnews # of Radio News Formats Used 0.0513 0.0542 1  0 0 cab_news # of Cable News Channels Viewed 0.0758 0.145 0.0961 1  0 0 0 any_daily Whether Daily Newspaper 0.0909 0.0092 0.1556 0.1182 1  0 0.0001 0 0 

Table 11: Correlations among Gap (News-Ent) Measures 

 TVgap igap radgap cab_gap any_da~yTVgap TV: News-Ent 1 Igap Internet: News-Ent -0.108 1  0 Radgap Radio: News-Ent. -0.0679 0.1417 1  0 0 cab_gap Cable: News-Ent. -0.0083 0.1264 0.1119 1  0.0005 0 0 any_daily Whether Daily Newspaper -0.0218 0.0021 0.0675 -0.0013 1  0 0.3706 0 0.5748 
74

Table 12: Do People Using One Medium More Use Other Media Less?  (1) (2) (3) (4)  TV Half Hours per Week # Radio Formats Used Weekly Internet Hours Daily Newspaper # # Radio Formats Used 0.0620 0.0572 0.0271  (0.0501) (0.0088)** (0.0008)** Weekly Internet Hours -0.1286 0.0041 0.0007  (0.0134)** (0.0006)** (0.0002)** Daily Newspaper 1.0543 0.2448 0.0826  (0.1505)** (0.0071)** (0.0265)** # Cable Channels Used 0.0937 0.0067 0.0758 0.0031  (0.0107)** (0.0005)** (0.0019)** (0.0002)** TV Half Hours per Week 0.0001 -0.0040 0.0003  (0.0001) (0.0004)** (0.0000)** Constant 28.0960 2.2279 2.9283 0.0399  (0.5341)** (0.0248)** (0.0944)** (0.0085)** Observations 178784 178784 178784 178784 R-squared 0.11 0.16 0.09 0.11 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1%. Regressions include individual controls for education, 
75

Table 13: Do People Using News from One Medium More Use News from Other Media Less?  (1) (2) (3) (4)  TV News Half Hours 

(4-12pm) 

# Radio News 

Formats Used 

# Internet News Uses Daily Newspaper 

# Radio News Formats Used 0.0726 0.0945 0.0611  (0.0304)* (0.0061)** (0.0024)** # Internet News Uses -0.0851 0.0141 0.0048  (0.0117)** (0.0009)** (0.0009)** Daily Newspaper 0.2909 0.0585 0.0310  (0.0298)** (0.0023)** (0.0060)** # Cable News Channels Used 0.2306 0.0092 0.1691 0.0233  (0.0133)** (0.0010)** (0.0027)** (0.0011)** TV News Half Hours (4-12pm) 0.0004 -0.0035 0.0018  (0.0002)* (0.0005)** (0.0002)** Constant 2.6885 -0.0962 0.5313 0.1256  (0.1023)** (0.0080)** (0.0206)** (0.0081)** Observations 178784 178784 178784 178784 R-squared 0.13 0.12 0.11 0.11 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1%. Regressions include individual controls for education, 
76

Table 14: The Relationship among News – Entertainment Gaps across Media  (1) (2) (3) (4) (5)  TV: News-Ent. Radio: News-Ent. Internet: News-Ent. Daily Newspaper Cable: News-Ent. Radio: News-Ent. 0.1459 0.0253 -0.0140 0.1848  (0.0408)** (0.0055)** (0.0008)** (0.0095)** Internet: News-Ent. -0.1766 0.0046 -0.0027 0.1586  (0.0175)** (0.0010)** (0.0003)** (0.0041)** Daily Newspaper -0.4370 -0.1230 -0.1302 -0.3693  (0.1210)** (0.0070)** (0.0164)** (0.0282)** Cable: News-Ent. 0.1089 0.0114 0.0535 -0.0026  (0.0101)** (0.0006)** (0.0014)** (0.0002)** TV: News-Ent. 0.0005 -0.0032 -0.0002 0.0059  (0.0001)** (0.0003)** (0.0000)** (0.0006)** Constant -22.5271 -2.3618 -2.7368 0.0710 -5.5855  (0.4341)** (0.0247)** (0.0588)** (0.0085)** (0.1011)** Observations 178784 178784 178784 178784 178784 R-squared 0.07 0.22 0.16 0.11 0.05 Notes: Standard errors in parentheses. * significant at 5%; ** significant at 1%. Regressions include individual controls for education, 
77

Table 15: Media Use and Local Isolation (Youth)  (1) (2) (3) (4) (5) (6) (7) (8)  TV Half Hours 

per Week 

TV News 

Half Hours 

(4-12pm) 

# Radio 

Formats 

Used 

# Radio 

News 

Formats 

Used 

Weekly Internet 

Hours 

# Internet 

Uses 

# Internet 

News Uses 

Daily 

Newspap

Young Dummy x % Young 

17.7282 0.7965 1.4439 0.2378 -5.0205 -4.4882 -0.3347 -0.1909 

 (7.7469)* (1.6857) (0.6156)* (0.3073) (2.0370)* (2.0037)* (0.4528) (0.1195) Young Dummy -29.0308 -5.7107 0.6771 -0.4433 5.0475 5.8596 0.7056 -0.2223  (3.5611)** (0.8013)** (0.2855)* (0.1464)** (0.9059)** (0.9139)** (0.2069)** (0.0541)** Constant 53.5560 8.7510 0.8253 0.2005 0.1374 -0.1971 -0.0515 0.4057  (0.7917)** (0.1823)** (0.0355)** (0.0107)** (0.1014) (0.1033) (0.0196)** (0.0089)** Observations 178784 178784 178784 178784 178784 178784 178784 178784 R-squared 0.10 0.13 0.16 0.14 0.08 0.16 0.09 0.11 Notes: Robust standard errors in parentheses – se’s are clustered on CMSA. * significant at 5%; ** significant at 1%. All specifications include age d

and a gender dummy.
78

Table 16: Media Use and Local Isolation (Blacks)  (1) (2) (3) (4) (5) (6) (7) (8)  TV Half Hours 

per Week 

TV News 

Half Hours 

(4-12pm) 

# Radio 

Formats 

Used 

# Radio 

News 

Formats 

Used 

Weekly Internet 

Hours 

# Internet 

Uses 

# Internet 

News Uses 

Daily 

Newspap

Black x % Black in Metro Area 

24.4042 0.9195 1.0901 0.0128 -0.4154 -0.9014 -0.2193 -0.0380 

 (5.0254)** (0.9571) (0.2897)** (0.0752) (0.7065) (0.5800) (0.1472) (0.0465) Black 4.8300 0.3711 -0.4620 -0.1101 -0.6671 -0.5818 -0.0835 -0.0072  (1.0996)** (0.1903) (0.0589)** (0.0140)** (0.1558)** (0.1257)** (0.0286)** (0.0110) Constant 30.8601 3.3045 2.1940 -0.1175 3.0567 3.7750 0.5251 0.1002  (0.7954)** (0.1807)** (0.0436)** (0.0197)** (0.1554)** (0.1498)** (0.0243)** (0.0084)*Observations 178784 178784 178784 178784 178784 178784 178784 178784 R-squared 0.11 0.13 0.16 0.15 0.08 0.16 0.09 0.11 Notes: Robust standard errors in parentheses – se’s are clustered on CMSA. * significant at 5%; ** significant at 1%. All specifications include age d

and a gender dummy.
79

Table 17: Media Use and Local Isolation (Hispanics)  (1) (2) (3) (4) (5) (6) (7) (8)  TV Half Hours 

per Week 

TV News 

Half Hours 

(4-12pm) 

# Radio 

Formats 

Used 

# Radio 

News 

Formats 

Used 

Weekly Internet 

Hours 

# Internet 

Uses 

# Internet 

News Uses 

Daily 

Newspap

Hisp. Dummy x % Hisp. 

4.5915 0.7486 1.5035 -0.1005 -3.1912 -2.6137 -0.3580 0.0805 

 (5.6911) (1.2777) (0.1913)** (0.0646) (0.7296)** (0.9577)** (0.1688)* (0.0566) Hispanic 2.7389 0.9868 -0.4575 -0.0700 -0.6133 -0.7520 -0.0787 -0.0845  (1.2216)* (0.2256)** (0.0364)** (0.0213)** (0.1490)** (0.1678)** (0.0324)* (0.0091)*Constant 30.9175 2.9045 2.2312 -0.0971 3.4554 4.1867 0.5670 0.1283  (0.7707)** (0.1155)** (0.0436)** (0.0144)** (0.1139)** (0.1146)** (0.0261)** (0.0083)*Observations 178784 178784 178784 178784 178784 178784 178784 178784 R-squared 0.10 0.13 0.16 0.15 0.09 0.16 0.09 0.12 Notes: Robust standard errors in parentheses – se’s are clustered on CMSA. * significant at 5%; ** significant at 1%. All specifications include age d

and a gender dummy.
80

Table 18: Summary of Statistical Results on Substitution  TV 

all use 

TV all outlets 

TV news use 

Radio all use 

Radio all outlets 

Radio news use 

Radio news outlets 

Dailies use 

Dailies outlets 

Weeklies use 

Weeklies outlets 

Internet use 

TV all use E D E H TV all Outlets TV news use 

 G G J 

Radio all use 

E 

Radio all outlets 

C C C 

Radio news use 

 J 

Radio news outlets 

 G A 

Dailies use 

 

Dailies outlets 

 A B 

Weeklies use 

 

Weeklies outlets 

 C A B 

Internet use 

E H 

 A 

Internet news use 

 F G 

H I 

 D A J 

Cable use E A A D A D Cable news use 

 G G J 
81

Symbols denote significance at 5% level (except as noted below). We register the correlation measures (A, E, F, G) symmetrically. Fo

regression coefficients, we treat the column as the dependent variable and the row as the independent variable. Key: A = correlation of changes on changes in aggregate data (see Part I, Table 12), listed if significant at 5% level B = regression of N on N in changes (agg data – see Part I, Table 13), listed if t-stat>1, bold if significant at 5% level C = regression of use on N in changes (agg data – see Part I, Table 14), listed if t-stat>1, bold if significant at 5% level D = regression of use on use in changes (agg data – see Part I, Table 15), listed if t-stat>1, bold if significant at 5% level E = correlation of overall use (micro data – see Part II, Table 9) F = correlation of news use (micro data – see Part II, Table 10) G = correlation of news-ent gap (micro data – see Part II, Table 11) H = micro regression of use on use (overall media use measures – see Part II, Table 12) I = micro regression of use on use (news measure – see Part II, Table 13) J = micro regression of use on use (gap measure – see Part II, Table 14)