The Dutch Radio Broadcasting Industry:
Market Expansion or Business Stealing
Ryanne van Dalen
Master Thesis - Research Master University of Groningen Faculty of Economics & Business
February, 2008
Abstract
1
Introduction
An increasing body of literature focuses on the two-sidedness of media markets. A major drawback of the empirical studies on two-sided models for media markets is the absence of detailed data on prices or quantities. For example, Berry & Waldfogel (1999) only have a single market advertising price for radio stations and have no data on the amount of advertisements being broadcasted. This paper uses unique firm-level data on market shares as well as on advertising prices and advertising quantities for the Dutch radio broadcasting industry. This novel dataset allows us to include more detailed information when estimating a two-sided model for the Dutch radio broadcasting industry.
The purpose of this study is to gain insight into the attributes that determine the demand for the radio broadcasting market in The Netherlands. The basic idea is to estimate a two-sided market demand which includes the demand of listeners as well as the demand of advertisers. Data is collected for the radio stations that use the spectrum to broadcast their programs in 12 regions over 23 two-monthly periods to estimate a nested logit model of demand for listening (Berry, 1994; Berry & Waldfogel, 1999; Train, 2003). The estimates will indicate whether there is market expansion or business-stealing in the radio broadcasting market.
The radio spectrum has historically been closely regulated because of possible interference between different radio services. The traditional command-and-control approached focused mainly on the technical aspects such as the avoidance of harmful interference and the technical optimization of spectrum. More recently, the economic approach to spectrum management has become more important due to the rapid growth of wireless communications and the ongoing technological developments. Therefore spectrum management is more oriented towards the maximization of the economic value of spectrum use given the existing supply of radio frequencies that are used and to give preference to the most valuable application (Falch & Tadayoni, 2004).
From this policy-making perspective, the degree of market expansion or business-stealing is also a highly relevant question because the switch from analogue to digital radio will take place in the near future.1 The results can not only give insight of the current market structure of radio broadcasting but can also be used to evaluate the possible effects of the introduction of digital radio. Besides offering better sound quality and more interactive features the introduction of digital radio will also imply that more radio stations can be allowed. However more radio stations does not
1The Dutch government announced in their annual plan for 2008 that the licenses for digital
necessarily mean that this does improve social welfare. Entry can be excessive in markets where products are substitutes and average costs are decreasing in output (Mankiw & Whinston, 1986). This result does not hold when the introduction of a new product increases product variety or if entry leads to a decrease in prices.
Moreover, radio broadcasting is also of great economic importance. In 2006 the average individual in The Netherlands spends 192 minutes listening to radio every day. Besides providing entertainment to individuals, radio stations are also important channels for advertising companies to market their product to (potential) consumers. The media advertising expenditures for radio stations were¿485 million in 2006 in The Netherlands, which is 5.8% of the total media expenditures.2
The remainder of this paper is organized as follows. Section 2 provides a litera-ture overview which includes theoretical and empirical literalitera-ture on media firms and two-sided markets as well as some economic aspects of radio spectrum. Section 3 gives a description of the radio broadcasting market in The Netherlands. The model for listener demand is presented in Section 4 and also discusses market expansion and business-stealing in the context of discrete choice models. Section 5 describes the data whereas the next section discusses the empirical specification and estima-tion methodology. In Secestima-tion 7 the demand estimates are presented and Secestima-tion 8 discusses the implications and gives some directions for future research. Finally, Section 9 concludes.
2
Literature review
The radio broadcasting industry can be characterized as a two-sided market because there are two distinct types of users that interact on a common platform. The two types of users in this market are listeners and advertisers whereas radio stations serve as an intermediary between them. Radio stations provide broadcasting of programmes and/or music to listeners and provide advertisers with a channel to market their products to potential consumers. An important aspect of two-sided markets is that utility obtained by one side of the market is affected by the number of participants the other side of the market. If a radio station attracts more listeners it will also attract more advertisers yet more advertisements are likely to decrease to number of listeners. Other common examples of two-sided markets are media firms such as television and magazines, credit card markets, shopping malls, and dating agencies.
2Commissariaat voor de Media, Medialandschap in beeld: Concentratie en pluriformiteit van
Armstrong (2005) and Rochet & Tirole (2003) provide theoretical models for two-sided markets. These papers focus on the pricing issue for various governance structures of two-sided markets and show that one side of the market is priced below marginal costs and the other side acts as the profit segment. The profit-maximizing outcome of the platform might even have negative prices or free services. The radio broadcasting market provides a good example of a platform that charges no price for one side of the market. Listeners can freely listen to radio and the profits for radio stations come from the charges to advertisers.
There are also a number of theoretical papers on two-sided markets for media firms. Anderson & Coates (2005) study the broadcasting industry to address the market failure in this market. They analyze how well broadcasters provide program-ming to viewers/listeners and potential consumers to advertisers. They find that the equilibrium level of advertising can be below or above the optimal level depending on the degree of nuisance of advertisements and the degree of substitutability of programmes. In a market with one or two programmes (or radio stations) the mar-ket under-provides advertising if the degree of nuisance is low whereas advertising will be over-provided when the nuisance of advertising is high. In addition, when programmes are close substitutes there is fiercer competition for viewers/listeners and under-provision of advertising becomes more likely. This finding is in line with the paper of Dukes (2004) who also shows that advertising levels are below optimal levels if programmes are close substitutes.
Kaiser & Wright (2006) empirically examine the pricing structure of two-sided markets in a media context. They find that the market for magazines in Germany can be characterized as two-sided in which the prices for the readers are subsidized and that the profit for magazines comes from the advertisers. Argentesi & Filistruc-chi (2006) employ a similar approach to measure the degree of market power in the Italian newspaper market. They find evidence of joint profit maximization for the cover price and competition in the advertising market by comparing the estimated markups with the observed markups.
direc-tories. The paper by Berry & Waldfogel (1999) empirically addresses the question whether free entry in the US radio broadcasting market leads to a socially excessive number of radio stations. The estimates indicate a welfare loss of 45% of revenue under free entry compared to the social optimum in radio broadcasting. Moreover in the free entry situation as is observed in the US there are 2,509 radio stations while the socially optimal number of stations would be 649.
The Ofcom3 has published several studies that focus on the economic impact
of the use of spectrum (2001,2002, 2006) where the economic impact is defined as the sum of producer and consumer surplus. In these studies producer surplus is calculated as net turnover minus economic costs and consumer surplus as the number of listeners times the individual listener benefit which is in turn derived from consumer willingness-to-pay surveys. For example, European Economics (2006), who carried out such a study for Ofcom, estimates the economic impact£42 billion for the UK in the year 2006. The value of total economic benefits for the broadcasting sector (television and radio) are£12 billion. In addition, the broadcasting and public mobile radio radio sectors account for almost three quarter of the estimated benefits of which consumer benefits account for around 80% and producer benefits and license fees for the remaining 20%.
The methodology used by Ofcom has also been used by Ecorys (2003) to esti-mate the economic impact of radio spectrum in The Netherlands. Ecorys finds an economic impact of ¿4,3 billion. However this study only includes a subset of the sectors that are included in the studies of Ofcom.4 Another rough measure for the economic value of radio spectrum in The Netherlands is provided by De Bijl (2004). By assuming that the ratio of total surplus to GDP in the UK5 is comparable to that of The Netherlands, the total economic impact of the use of radio spectrum is then estimated at¿9 billion. Falch & Tadayoni (2004) report a direct contribution from the radio spectrum of 1.2% to Danish GDP of which one third comes from equipment production and two thirds from service provision. Again mobile and broadcasting make up for around 80% of the economic value which is similar to the results of spectrum value in the UK.
This paper empirically examines whether the Dutch radio broadcasting mar-ket can be characterized by marmar-ket expansion or business stealing. It employs a
3Ofcom is the independent regulator and competition authority for the UK communications
industry.
4For comparison: The sub-sector radio broadcasting generated a total welfare of ¿0,6 million
in The Netherlands in 2003 and£1,051 million in 2000 in the UK.
5De Bijl uses the result of the report by the Radiocommunications Agency in 2002 which
demand specification that takes into account the two-sidedness of the radio broad-casting market by defining a demand function for listeners and a demand function for advertisers. This approach is also taken by Berry & Waldfogel (1999) for the American radio broadcasting market. However this approach has not yet been fol-lowed for the Dutch radio broadcasting industry. Moreover this paper builds on the work of Berry & Waldfogel by including more detailed data. Therefore this paper contributes to the existing empirical literature on media markets and its market structure.
3
The Dutch Radio Broadcasting Industry
In 1988 the first commercial radio stations were introduced in The Netherlands. Un-til then the public radio stations had a monopoly position in the radio broadcasting market. Although these commercial radio stations were still not officially allowed, they used a foreign operating licence to broadcast in The Netherlands. In 1992 the first Dutch commercial radio broadcasting license was granted and during this year the number of commercial radio stations increased to nine.
In the nineties the number of commercial stations kept increasing which is mainly due to the increased availability of frequencies for radio broadcasting from temporary license auctions in 1994 and 1997. At the end of the nineties a few commercial radio stations succeeded in obtaining a considerable market position and therefore the public radio stations saw their market shares decline. The Herfindahl-Hirschmann index (HHI) for the national radio broadcasting market decreased from 0.9 to 0.25 in the nineties and is still around 0.25 implying that the radio broadcasting market is very concentrated.6
The current assignment of frequencies for radio broadcasting are licensed by a beauty contest held in 2003.7 The Dutch government assigned a large number
of FM and AM licenses for commercial radio for a period of eight years. There were nine national allotments of which five have specific format requirement8 and
four for unrestricted programming. Moreover, there were 26 regional allotments for
6The HHI is calculated by P(s2
j) where sj is the listening share of radio station j. The HHI
ranges from 0 to 1 where values close to zero indicate a competitive market and values close to 1 indicate a monopoly.
7Each firm that wanted to participate in the beauty contest needed to submit a business plan
and a financial bid. A firm could score a ’+’ indicating above average or a ’0’ otherwise on its business plan. An allotment is assigned to a firm if he/she is the only firm with a ’+’ on the business plan. If more than one firm has an above average score the allotment is assigned to the firm with the highest financial bid.
8The format requirements are (1) not contemporary special music, (2) news/talk/information,
commercial radio stations. Besides these commercial radio stations there are five national public radio stations and thirteen regional public radio stations. These radio stations did not have to take part in the beauty contest and obtained the allotment for free. However there are stricter rules that the public radio stations need to obey in terms of coverage, advertisement, and format requirements.
From 2002 onwards the joint national market share has been higher for com-mercial radio stations than for public radio stations as can be seen from Table 1. However regional public radio stations still have the highest market shares in many provinces, especially in the north-eastern part of The Netherlands. Table 1 also shows that in 2004, the year after the beauty contest, total listening time increased with 12 minutes compared with 2003. However in 2005 the total time spent listening to radio during a day decreased again and remained the same as in 2006. It seems that the increase in the number of stations in 2003 only had a temporary effect on listening time.
Table 1. National market shares of radio stations 2001 2002 2003 2004 2005 2006 Public radio 45.4 45.8 45.9 44.0 42.8 43.9 - National 31.1 31.2 31.4 29.4 28.3 29.5 - Regional 14.3 14.6 14.5 14.6 14.5 14.4 Commercial radio 47.1 48.5 48.0 49.6 50.4 49.8 - National 44.8 45.6 45.6 47.0 47.6 47.3 - Regional 2.3 2.9 2.4 2.6 2.8 2.5 Other9 7.5 5.7 6.1 6.4 6.8 6.3
Total listen time 189 188 187 199 192 192
Source: RAB/Intomart GfK (00-24 hour/population 10+) & Mediaconcentratie in Beeld 2006
The total media expenditures for radio have increased with 65% in the period from 2001 to 2006.10 Although the market shares are almost split evenly between commer-cial and public radio stations, media expenditures have been higher for commercommer-cial radio stations. The commercial share of media expenditures has been around 70% and the public share around 30% during the last years. It is not surprising that the public share of media expenditures is substantially lower because public radio
stations have stricter requirements in their licenses than commercial radio stations with respect to the amount of advertising that is allowed.
The Dutch radio broadcasting market reached nearly 70% of the Dutch popula-tion above the age of 10 and on average an individual listens 192 minutes to radio every day in 2006. Morever 95.5% of the Dutch households has a radio and the aver-age household had 2.4 radios. Listening to radio can be characterized as side-activity as 95% of the total listening time is spent while performing other activities.11 The
main activities during the time spend on listening to radio are working, household activities, and travelling by car.12 Individuals listen to the radio mainly between
7am and 8pm with a peak around midday.
4
A discrete choice model of listener demand
This paper employs a discrete choice model for the listener demand which is widely used in the recent literature on applied economics to estimate the demand for differ-entiated products. This methodology is also applied to examine the economic effects under a variety of assumptions such as mergers, innovations, and the introduction of new products (Nevo, 2001; Petrin, 2002; Ivaldi & Verboven, 2005).
Discrete choice models assume that individuals choose either to listen to one of J radiobroadcasting stations, j = 1, ..., J , or choose the outside option j = 0, i.e. not to listen to radio at all. Following Berry (1994), the utility of listener i from listening to radio station j is given by13
uij = δj + ij = xjβ + ξj+ ij (1)
where δj is the mean utility of radio station j and is common to all consumers and
ij is the stochastic component of utility. The mean utility consists of observable
characteristics, xj, and of unobserved (by the researcher) product characteristics,
ξj. The utility of the outside option, j = 0 is normalized to zero for all consumers.
A consumer is assumed to choose to listen to the radio station that gives him or her the highest utility. That is, conditional on both the observed and unobserved (by the researcher) characteristics of the radio station, listener i will choose to listen to radio station j if and only if the utility obtained from radio station j is greater
11http://www.tijdsbesteding.nl/hoelanghoevaak/vrijetijd/media/geluiddragers/algemeen/20061018.html 12Carat, Mediafeitenboekje Nederland 2006
13In general, the utility of consumer i from consuming good j also depends on the price of the
than the utility that is obtained from listening to any other radio stations that is available.
The assumption on the distribution of unobserved consumer heterogeneity has important implications for the substitution patterns (see Appendix A for more de-tails). The next two subsections describe two specific cases which are the logit and the nested logit model. A detailed discussion about the mixed logit or random-coefficients logit model can be found in Nevo (2000).
4.1
The logit model
The simplest distributional assumption is that consumer heterogeneity enters the model only through the separable additive random error term, ij. The logit model
is obtained by assuming that each ij is identically and independently distributed
(iid) across radio stations and listeners with an extreme value distribution. The utility of listener i from radio station j is given by equation (1). The market share for radio station j is given by the probability that radio station j is chosen (see for example Train (2000) for a derivation of the logit probabilities). The market share of radio station j is given by:
sj =
eδj PJ
j=0eδk
(2) The assumption that the utility function is separable into two components, one de-termined by the characteristics of a radio station (δj) and one determined by listener
characteristics (ij) can lead to unrealistic substitution patterns in certain
applica-tions. More specifically, the logit model exhibits restrictive substitution patterns as can be seen from the ratio of two market shares:
sj sk = eδj PJ j=0eδj eδk PJ j=0eδj = e δj eδk
This ratio does not depend on any alternatives other than radio station j and k and is therefore said to be independent from irrelevant alternatives (IIA). In addition, the IIA property implies proportional substitution; improving one alternative is go-ing to draw necessarily from the other alternatives in proportion to the probability of these other alternatives. This property can be illustrated with a simple example. Suppose that there are two alternatives available in a market which are radio station A and the option of not listening to radio. Radio station A is assumed to have the following characteristics: xA = (1, 0, −1). Assuming that β = (1, 1, 1)0 the utility of
uiA= x0Aβ = 0.5 1 −0.5 1 1 1 = 0 The market share for radio station A is:
sA = eδj P1 j=0eδj = e 0 e0+ e0 = 1 1 + 1 = 1 2
Suppose now that radio station B enters the market with attributes xB = (1, −1, 0).
The utility of listening to each radio station is equal to zero and the market shares become: sA= sB = e0 e0+ e0 + e0 = 1 3
So the standard logit model predicts that listeners will substitute for radio sta-tions A and the outside option to the same extent. However radio station B is a closer substitute to radio station A and one would therefore expect that some of the listeners who prefer radio station A are more likely to switch to radio station B and that the listeners who prefer not to listen to radio are more likely to stay with their choice of the outside option. Another important aspect is that total listening has increased from 50% to 66.67% because of the entry of radio station B. So radio station B attracts listeners from the share of population not listening to radio. This effect is known as market expansion and takes place when listeners perceive radio stations to be differentiated products.
4.2
The nested logit model
In the nested logit model all radio stations are grouped into predetermined mutually exclusive groups or ’nests’ and the random term, ij, is decomposed into an iid
shock and a group-specific component. This decomposition allows the variance of the random terms in the utility function to be different across nests. The radio stations are grouped into G + 1 nests, g = 0, 1, ..., G. The outside good, j = 0, is assumed to be the only member of group 0. The utility of listener i from listening to radio station j in nest g is:
uij = δj + ζig + (1 − σ)ij = xjβ + ξj+ ζig+ (1 − σ)ij (3)
where δj is the mean utility which is common to all listeners and ij is iid with
stations and represents the preference of listener i for radio stations in group g. Cardell (1997) shows that the distribution of ζig is the unique distribution such that
if ij is an extreme value random variable, then the term ζig+ (1 − σ)ij also has an
extreme value distribution. The parameter σ lies between 0 and 1 and is a measure of correlation of listener utility across radio stations belonging to the same group. If σ = 1 there is perfect within group correlation of listener preferences and so the radio stations within this group can be perceived as perfect substitutes. When σ approaches 0, the within group correlation of utility also goes to 0 and the nested logit specification of utility becomes the standard logit utility specification given in equation (1).
The market share of radio station j in group g can be expressed as the product of the probability of choosing radio station j given that nest g is chosen and the probability of choosing nest g. That is, sj = sj|g · sg where sj|g is the within-group
market share and sg is the group share. This gives a market share of:
sj = eδj/(1−σ) P j∈geδj/(1−σ) · [ P j∈geδj/(1−σ)]1−σ [1 +PG g=1 P j∈geδj/(1−σ)]1−σ (4)
where the first term is the within-group market share and the second term is teh group share. The nested logit specification allows for more flexible substitution patterns than the logit model because listener heterogeneity is correlated across radio stations. More specifically, the correlation between radio stations within a group is higher than across groups. This can be shown by comparing the ratio of market shares for two radio stations that belong to the same group and for two radio stations that do not belong the same group. The ratio of market shares for radio station j and k that are not in the same group, j ∈ g and k ∈ g + 1, is:
sj sk = e δj/(1−σ)D−σ g eδk/(1−σ)D−σ g+1
and the ratio of market shares for radio station j and k that are in the same group, j, k ∈ g, is: sj sk = e δj/(1−σ) eδk/(1−σ)
(IIN). If radio station j and radio station k are in the same nest, the ratio of market shares is independent of all other radio stations. This implies that there is pro-portionate substitution within a group. Therefore IIA holds within groups but not across groups.
The substitution patterns of the nested logit model can be illustrated with the example that is also given in the logit case. In the situation where radio station A is the only radio station available in the market and σ is assumed to be equal to 1, the market share of radio station A is:
sA= e0 e0 · (e0)1−1 e0+ (e0)1−1 = 1 1· 1 2 = 1 2
which is thus similar to market share predicted by the logit model. However, if radio station B enters the market and radio station A and radio station B are assumed to be in the same nest the market shares become
sA= sB = e0 e0+ e0 · (e0+ e0)1−1 e0+ (e0 + e0)1−1 = 1 2· 1 2 = 1 4
The market shares indicate that radio station B steals its listeners from radio station A because total listening has remained 50%. This effect is referred to as business-stealing and exists when an additional station does not increase the share of population listening to radio. Rather it steals business from incumbent radio stations and radio stations simply split the ’pie of listeners’. The business-stealing effect occurs when radio stations are identical and listeners are are indifferent between the radio stations.
4.3
Business-stealing and market expansion
Table 2. Hypothetical market shares
σ = 0 σ = 1
Before After Before After Radio station A 0.5 0.33 0.5 0.25 Radio station B na 0.33 na 0.25 Outside option 0.5 0.33 0.5 0.5 Total listening 50 66.66 50 50
Table 2 shows that total listening increases when σ = 0 because each radio sta-tion gives idiosyncratic benefits. Total listening stays the same when σ = 1 because each radio station that is in the same nest is identical. The entrance of radio station B only draws listeners away from radio station A and does not attract any new listeners.
If σ = 0, so the nested logit is equal to the logit model, the introduction of a new radio station draws proportional from all other alternatives including the outside option. Therefore the introduction of a new radio station increases total listening and there is market expansion. In the nested logit model in which σ = 1, the introduction of a new radio station draws proportional from the alternatives that are correlated with this new radio station. That is, there is proportional substitution with radio stations that have similar characteristics as the new radio station and the market can be characterized by business-stealing. As σ goes to 1, the business-stealing effect is complete and an additional station does not increase total listenership. For smaller values of σ, total listenership increases in the number of radio stations with a maximum rate of increase when σ is 0.
5
Data
The dataset comprises of information on market shares, advertising prices, adver-tising quantities, station characteristics, and demographics for each market. The dataset covers 38 radio stations in 12 regions for 23 time periods, that is from the first two-month period (January-February, February-March, etc) of 2005 to the last two-month period of 2006. The radio stations that are included in this study are radio stations that utilize the spectrum to broadcast their programs. Some radio stations are excluded because of lack of information on either ratings data or adver-tising data.
The region Flevoland is not included because the response rate is too low to produce reliable ratings data.
The ratings data come from Intomart GfK who provides data on the Dutch tele-vision audience measurement (SKO) and radio audience measurement (CLO). The CLO ratings data are generated from listening diaries submitted by panel partic-ipants. Intomart GfK receives listening diaries from roughly 10,000 particpartic-ipants. The participants record their quarter-hour listening behaviour during one week. In-tomart GfK releases ratings data on a monthly basis over a two-month period. These ratings data are from Monday-Sunday, 7am to 7pm for the 10+ population and are available for 12 regions in The Netherlands. These regions are defined on the basis of the presence of a public regional radio station. The ratings data also provide in-formation on the 10+ population in each region. The most important ratings data is the Average-Quarter-Hour persons which is the average number of listeners to a particular radio stations for at least 8 minutes during a 15-minute period.
This data is enriched by information on advertising prices that come from the websites of the radio stations, the website www.radionieuws.nl, and by contact-ing radio stations. The advertiscontact-ing price used in this study is the spotprice. The spotprice is the advertising price per second multiplied by the average length of an advertisement (which is 20 seconds). Information on advertising revenues is supplied by Nielsen Media Research who monitors and publishes advertising revenue of radio stations on a montly basis. Advertising quantities are therefore calculated as the amount of advertising revenue divided by the spotprice.
The station characteristics that are available are costs and format and come from Agentschap Telecom which is the Radiocommunications Agency in The Netherlands and from the website www.rab.fm. The cost variable includes the costs that radio station pay to Agentschap Telecom which depend on the number of towers and the amount of wattage. The only demographic characteristic that is available for each market in every time period is economic climate which is an index used by the Central Bureau of Statistics in The Netherlands. This index is based on two questions concerning the economic situation over the past 12 months and the future 12 months. Negative values indicate that individuals perceive the economic situation as bad whereas positive values indicate good economic times. Appendix B gives an overview of the variables used in this study and the data sources.
During an average 15-minute period, 21% of the population listens to radio for at least 8 minutes. This figure includes public and commercial radio stations operating from both inside and outside the market. Although not reported in Table 3, the vast majority of listening, 20.68 percentage points of the 21%, is to stations from broadcasting inside the region.
Table 3. Description of main market-level variables
Variable Mean Median Standard deviation Minimum Maximum
AQH-persons 247720.2 220600 139244.8 55600 551100 AQH-rating 21.07 21.04 2.09 16.36 26.3 - Commercial 10.51 10.58 1.55 6.59 14.41 - Public 10.56 10.49 1.78 6.87 14.62 AQH-share 89.99 90.95 3.41 78.09 95.65 Number stations 21.31 21 1.49 17 27 Ad price 29.70 26.81 16.1257 6.97 69.52 - Commercial 30.09 16.10 41.00 .09 297.83 - Public 29.20 8.57 41.65 .22 211.85 Ad revenue 154503.8 134849.2 101860 22454.74 479604.6 Ad quantity 3733 3679.03 754.41 2254.58 6025.65 - Commercial 4815.31 4202.08 3022.73 93.13 15225.59 - Public 2350.95 1150.51 2259.42 11.68 7393.36 Economic climate -7.65 -15 19.61 -41 37 Costs 36146.74 13943.94 67418.49 1431.598 277622
The average advertising price of a radio station in a market is ¿29.70. The ad-vertising price is slightly lower for public radio stations than for commercial radio stations and commercial radio stations have the highest advertising price in a mar-ket. The average advertising revenues in a market are equal to ¿154,504 per radio station where the minimum amount of advertising revenues is in the region Zee-land and the maximum amount of ad revenues in Noord-HolZee-land. Although the AQH-rating and advertising prices are almost equal for commercial and public radio stations the quantity of advertisements is substantially higher for commercial radio stations. The minimum number of radio stations per market is 17 and the maximum is 27 with an average of 21.31. The markets in the sample have an average economic climate of -7.65 and the costs are on average 36146.74 per radio stations and vary greatly across markets.
Figure 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● 18 20 22 24 26 1e+05 3e+05 5e+05
Stations and Listening by Market
Number of Stations
Total AQH−persons
Number of Stations in a Market
Number of Stations % of Markets 18 20 22 24 26 0 20 40 60 80 ●● ● ● ●●●●●● ●●●●●●●●●●●●● ● ● ● ●●● ●● ●● ●● ●● ●●●●●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ●●●●●●● ●●●●●●●●●●●●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ●● ●● ●● ● ● ● ●● ● ●●●●●●●● ●●● ●●● ● ● ● ● ●●● ● ●● ●●●●● ● ● ● ●● ●●●●●●● ● ●●●●●●●●●●●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ●● ● ● ● ● ●● ● ●●●●●●●● ●●
1e+05 3e+05 5e+05
200
600
1000
Total Listening and Ad Price by Market
Total AQH−persons
Ad Price
Listen Share by Market
Total AQH share
% of Markets 80 85 90 95 0 20 40 60 80
6
Empirical Specification
This section presents the model of demand for the radio broadcasting industry which takes into account the interaction between the two sides of the market. In partic-ular, there are two demand specifications, one for the listener’s side and one for the advertising side. The nested logit specification is used to estimate the listener demand function and an inverse advertising demand is specified for the advertisers’ side of the market.
The nested logit model is a popular choice in empirical research on differenti-ated products because it is computational simple. This computationally simplicitly, however, comes at a cost because it places somewhat restrictive assumptions on the substitution patterns. Therefore, recent empirical research on differentiated prod-ucts uses the random coefficients model (Nevo, 2000; Berry, Levinsohn & Pakes, 2004) which allows for more flexible own and cross-price elasticities. Although the nested logit model has some restrictive substitution patterns, this method is pre-ferred because it allows for the explicit estimation of the degree of market expansion or business-stealing which is the main interest of this paper. Moreover the substi-tution patterns assumed in the nested logit model are far less restrictive than the substitution patterns assumed in the logit model as is explained in section 4.
6.1
Listener’s demand
The nested logit model classifies the radio stations into G groups and one additional group for the outside option. Radio stations within the same group are assumed to be closer substitutes than radio stations from different groups. In the case of the radio broadcasting market, it seems reasonable to assume that the outside option is represented by not listening to radio and constitutes one group (not listening to radio) whereas the other group consists of listening to radio. This structure assumes that the choice of listeners is twofold. First, an individual decides whether or not to listen to radio, then he or she decides to which radio station to listen among the radio stations that are available on the market. This means that listeners are more likely to switch to other radio stations than to turn off the radio.
(2) is the following:
ln(sjt) − ln(s0t) = α1ln(Ajt) + βXjt+ σ ln(sjt|g) + jt (5)
where the subscripts jt correspond to radio station j in market t. Following Nevo (2000), a market is defined as a region-two-monthly combination. In this specifica-tion the term α1ln(Ajt) + βXjt+ jt captures the mean utility of listening as opposed
to not listening whereas σ is the substitution parameter. The term Ajtis the amount
of advertising by radio station j in market t. The elements in Xjt are observed
char-acteristics and include regional dummies, a proxy for the economic situation in a market, format dummy variables, a dummy for radio stations with national reach, a dummy for public radio stations, and the costs of the radio station. The term jt is
a market-specific quality characteristic of radio station j that is unobserved to the researcher.
The market share of radio station j in market t is denoted by sjt. The market
shares are defined over the potential market size, Mt, which is, in accordance with
industry practice (Intomart GfK), the total population aged 10 years and above for every region in The Netherlands. So if radio station j has qjt listeners in market t
then the market share for this radio station is sjt = qjt/Mt. The market share of
the outside option is defined as the total market size minus the sum of the listeners of all radio stations relative to total market size, that is, s0t= (Mt−PJj=1qjt)/Mt.
The market share of radio station j in group g in market t is defined as the number of listeners to radio station j relative to the total number of listeners in group g. So the within market share is equal to sjt|g = qjt/PJj=1qjt.
The within group market shares (sjt|g) are by definition endogenous and need
to be instrumented. It is common in the literature on discrete choice models of product differentiation to use the average of the characteristics of radio stations in the same market as instruments (Berry (1994); Nevo (2000)). This paper uses the average costs of competing radio stations as an instrument. In addition, the number of competing radio stations is also used as an instrument for the within group market shares. Both instruments use the characteristics of radio stations that have the same format because these radio stations are the most direct competitors. These are appropriate instruments because they are both exogenous to the decision of a single radio station but are correlated with the within group market share.
The amount of advertising, Ajt, is also expected to be correlated with jt. If for
the amount of advertising. If listeners listen longer to a radio station they are also more likely to be exposed to advertisements. The listener demand function given in equation (5) is estimated by two-stage least squares (2SLS).
6.2
Advertising demand
A radio station produces revenue by ’selling’ its listeners to advertisers. Berry & Waldfogel (1999) assume a constant elasticity specification for the inverse advertising demand curve. This implies that the marginal willingness to pay for listeners declines in the number of listeners. This functional form is not well suited for the data in this study as can be seen from Figure 1. Therefore the approach by Rysman (2004) is followed in which the advertising price increases in market shares because this provides a better fit with the data. The inverse advertising demand curve is specified as:
pjt = Aγjts α2
jt πjt
where Ajt is the amount of advertising by radio station j in market t, sjt is the
market share of radio station j in market t, and πjt is the profit of advertisers from
the number of times a person is confronted with an advertisement. The parameter α2 is expected to be positive to allow the advertising price to increase with listening
shares. In practice, the inverse advertising demand curve is specified as a log-linear function:
ln pjt = γ ln(Ajt) + α2ln(sjt) + Xjtβ + νjt (6)
In this specification the term ln πjt is captured by a linear function of observable
variables, Xjt, and an unobservable term νjt. The advertising price that is used
in this paper differs from the specification of Berry & Waldfogel (1999). Berry & Waldfogel (1999) define the advertising price as the annual advertising revenue per listener. They construct this measure for advertising price based on total revenue figures for each market and therefore calculate a market price for advertising. In contrast, this paper has a unique price for each radio station in each market because our dataset has detailed firm-level information on advertising prices. However, this is a price for the entire reach of a radio station and therefore needs to be adjusted to take into account the differences in reach among radio stations. Suppose that radio station j has an advertising price of pj than the advertising price for radio station
j in market t is defined as pjt = pj ∗ qjt/Qj where Qj is the sum of the listeners to
radio station j on all markets for the same time period.
ln(sjt) is expected to be correlated with the term νjt. The amount of advertising,
Ajt, is also instrumented for because if for some unobservable reason advertiser’s
willingness to pay would be high the amount of advertising is also expected to be high. Therefore instruments are used for the market share and the amount of advertising of radio station j and include almost the same as in the listener demand specification. The only difference is that for the market shares the total number of competing radio stations is used as an instrument rather than the number of competing radio stations with the same format. When the number of competing radio station with the same format is used as an instrument for the amount of advertising the test of valid instruments cannot be accepted. This implies that on the advertising side of the market the radio stations are not perceived as different groups. Rather advertisers are interested in the share of population that listens to a specific radio station and do not prefer a particular kind of format. Again 2SLS is used to estimate equation (6).
7
Results
This section presents parameter estimates from the model and discusses the impli-cation of these estimates. Table 4 shows the estimation results for the listen demand equation and ad price equation.
The amount of advertising is positive implying that radio stations that advertise more have higher market shares. This is in contrast with the view that listeners dislike advertisements. There might be two reasons for the positive effect of adver-tising. First, the amount of advertising can be indicative for the quality of the radio stations. Radio stations with higher market shares might be able to attract more advertisers because of their greater reach. This effect then dominates the negative effect of advertisements to listeners. Secondly, switching to another radio station is not as likely as in other media channels. For example, it is easier to switch to another television channel or to skip advertisements in magazines or newspapers. Moreover listening to radio is performed as a side activity while watching television and reading magazines or newspapers is a main activity. Furthermore, radio stations often have similar advertisement slots especially around the hourly broadcasting of the news. Therefore, switching from radio station will often lead to being exposed to advertisements broadcasted by other radio stations.
The parameter of main interest is σ and is .894 indicating that the radio broad-casting market can be characterized by business-stealing. This implies that radio stations are perceived as strong substitutes by listeners and that an additional sta-tion will only slightly increase total listening. The meaning of the estimate of σ can be illustrated with the following example. Suppose that there is 1 radio station in a market attracting 10% of the population as listeners, that is, sj = 0.10. If the
parameter σ is equal to 1, the entrance of a similar radio station has no effect on overall listening. Therefore, after entry the 2 radio stations will share the ’pie of listeners’ and both have a market share of 5% and the outside option s0 remains
90%. If σ is equal to 0, which is the logit case, the entrance of a new radio station increases overall listening to 18.2%. The parameter σ is found to be roughly equal to .9 giving a post-entry market share of
eδj/(1−σ) P j∈geδj/(1−σ) · [ P j∈geδj/(1−σ)] [1 + (PG g=1 P j∈geδj/(1−σ))1−σ] = e −2.197/0.1 2 · e−2.197/0.1· 20.1· e−2.197 1 + 20.1· e−2.197 = .053
where the mean utility, δj, is derived from equation (4) by inserting the pre-entry
market shares and number of stations. Both radio stations have a market share of 5.3% thus total listening has only slightly increased from 10% to 10.6%. Going from 1 radio station to 10 radio stations makes total listening increase to 12.3% and going from 1 to 25 radio stations makes total listening increase to 13.3%.
implying that in bad economic times advertising prices are also lower. Advertisers are not willing to pay a high price for their ads as they will not be as effective as in good economic times. National radio stations have lower ad prices than regional radio stations and radio stations with a rock format also have a lower ad price than radio stations with one of the included formats (pop, news, 70’s, 80’s & 90’s). The costs for towers and wattage are positively related with ad prices. The reason for this finding can be twofold. First, higher cost implies higher prices to cover these costs. Secondly, higher cost means better coverage and therefore greater reach which is important when advertisers make their choices.
Remarkable findings are that the quantity of ads is positively related with ad prices and that market shares are negatively related with ad prices. This would imply that radio stations that attract a large part of the regional population as listeners have lower ad prices than radio stations with only a small listening share. The next subsection will go more in detail about this finding and estimates two alternative specifications of the nested logit model.
The Wu-Hausman test rejects the null hypothesis that the log of the within-group share and the amount of advertising are exogenous. For the listen demand function the test statistic is 130.712 and for the ad price equation the test statistic is 2290 both with a p-value of .000. The Pagan-Hall test rejects the null hypothesis of homoskedastic error terms at the 5% level for both equations and therefore robust standard errors are used throughout this paper. For both equations the Anderson canonical correlations likelihood-ratio test rejects the null hypothesis of underiden-tification at the 1% level while the Hansen J test does not reject the null hypothesis of valid instruments. In the first-stage regressions of listen demand and ad demand the instruments for the log of the within-group share and the log of the quantity of ads are jointly significant at the 1% level. Table C in appendix C shows the results of these tests.
17.894 leads to the rejection of the null hypothesis. This raises some doubt about the validity of the instruments. However, this is the only time that the hypothesis of valid instruments cannot be accepted.
7.1
Alternative specifications
The result from the previous section are based on a nested logit model with two nest, that is, one nest listening to radio containing the radio stations and one nest containing the outside option. This implies that that there is proportional sub-stitution among all radio station regardless of their characteristics. An important characteristic of a radio station is its format. To take into account the different formats a nested logit model is estimated where the grouping of radio stations is based on their format. In addition, the demand side will also be estimated when only commercial radio stations are included.
Nests according to format This specification uses the same 2SLS estimation procedure but with the sole exception that the grouping of radio stations is given by format rather than by listening and not listening. In total, there are five nests which are pop, news, rock, 70’s,80’s & 90’s, and not listen to radio. This grouping by formats allows the tastes to be correlated within format so that a listener to a pop radio station is more likely to stay within the pop format than to choose to listen to a radio station with another format. The choice of a listener is still twofold but he or she first chooses a format and then a radio station within that format. Whereas σ measured the correlation within the nest listening to radio in the previous specification, it now measures the correlation of format-specific tastes. If σ = 1 the formats also have equal market shares after the entry of a radio station in the nest pop and therefore the pop radio stations share the listeners listening to pop radio. On the other hand, if σ = 0 the entry of a pop radio station does not change the ratio of market shares. This implies that the group share of the pop format increases. The within-group share sjt|g therefore becomes the number of listeners to
radio station j divided by the total number of listeners to the format where radio station j belongs to. That is, sjt|g = qjt/Pj∈gqjt. For simplicitly, σ is assumed to
be equal across formats.
in magnitude except for the coefficients of σ which is now .66. This implies that in hypothetical market with 1 radio station in each format and each with a market share of 5% that the entry of a new radio station in the format pop leads to a market share for the pop radio stations of
e−2.912/0.34 2 · e−2.912/0.34 ·
20.34· e−2.912
1 + 50.34· e−2.912 = .031
and the market shares of the radio stations not belonging to the group pop becomes e−2.912/0.34
e−2.912/0.34 ·
e−2.912
1 + 50.34· e−2.912 = .0497
Total listening increases from 20% to 21.1% and total listening to pop radio stations increases from 5% to 6.2%. However, the post-entry share for the incumbent pop radio stations has decreased from 5% to 3.1%. The parameter σ therefore now takes into account the format-specific preferences of listeners. The new pop radio station attracts most of its listener from the incumbent pop radio station and only a few from the other nests including not listening.
The format dummies are significantly positive implying greater listenership to these formats compared to radio stations with a rock format. The average market share for the format rock, pop, news, and 70’s, 80’s, & 90’s music are 1.1%, 6.2%, 6%, and 7.8%, respectively. This suggests that the formats pop, news, and 70’s,80’s & 90’s formats are more popular than the rock format in The Netherlands and supports the findings of the parameters estimates of the format dummies. The amount of advertising is also positively related with market shares.
For the listener demand equation the Anderson canonical correlations likelihood-ratio test rejects the null hypothesis of underidentification at the 1% level while the Hansen J test reject the null hypothesis of valid instruments at the 5% significance level (p-value of .042). This suggests that the instrumental variables are correlated with unobservable factors. However the instruments for the instruments for the log of the within-group share and the log of the quantity of ads are jointly significant at the 1% level. The results of the test statistics are reported in Table C in Appendix C.
commercial radio stations who are assumed to maximize their profits. It can be argued that public radio stations therefore do not maximize profits but maximize a weighted function of profit and quality of some other non-profit argument. Further-more the public radio stations did not have to participate in the beauty contests to obtain an operating license. This gives them a great cost advantage because the commercial radio stations paid large amounts to obtain an operating license. For example, the average bid of the beauty contest in 2003 was around ¿35 million for the national allotments.14 Although the public radio stations obtained their license
for free they do have to pay the yearly costs for towers and wattage just as the commercial radio stations.
The results for the commercial radio stations are reported in column 7 and column 8 of Table 4. The market shares are now defined as follows: sjt,c = qjt,c/Mt,
s0t,c = (Mt−P qjt,c)/Mt, and sjt|g,c = qjt,c/P qjt,c where the subscript c refers to
commercial radio stations. The public dummy and format dummy for news are no longer in the regression because those consisted of only public radio stations.
The parameter estimate for σ is again relatively high and equals .729 suggesting that commercial radio stations are strongly substitutable and that the market for commercial listening can also be characterized by business stealing. It is not sur-prising to find a lower value for σ because only half of total listening is included in this model because of the exclusion of the public radio stations.
The other parameter estimates for the listener demand function are also highly significant except for the northern region. The coefficient of the eastern region is the only one that changes sign compared to the model with all radio stations included. This suggests that listeners in the eastern part of The Netherlands tend to listen more to public radio stations than do listeners in the southern and western part. The dummy for national radio stations is significantly positive indicating that listeners prefer national commercial radio stations to regional commercial radio stations. The format dummies for pop and 70’s, 80’s & 90’s music are again significantly positive. The effect of advertising is still positive implying that commercial radio stations that advertise more have higher market shares.
The signs of the coefficients of the ad demand function are similar to the previous estimations. However, the estimates for economic climate and the northern region have turned insignificant. Moreover, the estimates are larger in magnitude especially the format dummies. The effect of the format pop and 70’s,80’s & 90’s on prices is quite large compared to the rock format. The negative impact of market shares on prices has not disappeared which therefore cannot be attributed to presence of public
radio stations. The results do not provide evidence that public radio station require a different modeling approach or should be excluded at the demand side because they face different market condition namely with regard to entry (the operating license). These considerations should be taken into account when one wants to specify the cost side which is an interesting issue that is beyond the scope of this study.
The specification with all radio stations is preferred because the estimates provide evidence that the presence of public radio stations does not influence the results at the demand side and gives a better representation of total listening in a market. This is also motivated by the detailed data that this study uses which makes it possible to create market prices and costs for radio stations that operate in several markets and also to include outmetro radio stations. For example, Berry & Waldfogel (1999) include only inmetro commercial stations and use a market advertising price and assume symmetry of radio stations which is probably mainly due to a lack of detailed data.
The Wu-Hausman test again rejects the null hypothesis that the log of the within-group share and the amount of advertising are exogenous for both equations. The Pagan-Hall test rejects the null hypothesis of homoskedastic error terms and there-fore robust standard errors are used. For both equations the Anderson canonical correlations likelihood-ratio test rejects the null hypothesis of underidentification at the 1% level while the Hansen J test does not reject the null hypothesis of valid instruments. In the first-stage regressions of listen demand and ad demand the in-struments for the log of the within-group share and the log of the quantity of ads are jointly significant at the 1% level. (See Table C in appendix C for the results of the test statistics).
8
Implications & Further Research
number of radio stations would be restricted. The study by Rysman (2004) also finds a value of .8 for σ but shows that more directories in the market for Yellow Pages leads to higher welfare. This result can be explained by the positive network found in the market for Yellow Pages meaning that more advertising leads to more consumer usage of Yellow Pages which in turn leads to more advertising.
This study also finds a positive effect of advertising on market shares which is not in line with listeners having disutility from advertisements. A possible interpreta-tion for this positive effect, that is persistent throughout the different specificainterpreta-tions, is that the amount of advertising is also a proxy for quality or popularity because radio stations that are able to attract more advertisers are those that also attract more listeners. So radio stations have higher market shares by offering more pop-ular or mainstream music or having good broadcasting content and, in turn, these higher market shares attract more advertisers. The negative impact of market shares on advertising prices is very counter-intuitive. The only explanation is that radio stations with low market shares have relatively high advertising prices.
Intuitively, the introduction of digital radio in the near future is not expected to have a positive impact on people’s decision to listen to radio by allowing more radio stations. Social welfare is expected to decrease when the number of radio stations increases because listeners perceive radio stations to be close substitutes. Consequently, a new radio station does not increase variety. This implies that a new radio station brings additional costs to the radio broadcasting industry but no positive externality. Although we find a similar result as Rysman (2004) with respect to the impact of advertising on market shares, a crucial difference is that consumers search for advertisements in the market for Yellow Pages because they use directories to find information whereas in the radiobroadcasting market listeners tune in to a station to be entertained with music or other content.
More radio stations are also expected to result in fiercer competition among radio stations because hardly any new listeners are attracted by the entrants. A good example is the case of the UK radio broadcasting industry where digital radio has already been introduced. The digital radio take-up per household has increased from 1% in 2001 to 16% in 2006. During the same period the total number of analogue and digital radio stations increased from 345 to 389 but the average weekly hours spent on listening has remained very stable around 20 hours per head.15 This means that there is fierce competition among radio stations in the UK for listeners.
Moreover, radio stations that use the spectrum to broadcast their programs generate their revenue from the advertising side of the market and thus cannot be
profitbale without broadcasting advertisements. An interesting trend is the gaining popularity of radio stations that broadcast their music on the internet. The average weekly hours listened to radio has increased from 21.5 hours in 2005 to 22.7 hours in 2006.16 This increase is due to the increase in online listening to radio as the traditional ways of listening to radio have remained stable. The internet radio stations do not have to obtain a license to broadcast their music on the internet and do not to have to obey any requirements other than copyright laws.
It should be noted however that other features of digital radio are not taken into account in this study which might have an impact on the current market structure. For example, digital radio also improves sound quality and offers more interactive features for listeners. These aspects might attract more listeners especially if this allows radio stations to differentiate themselves.
An interesting topic for future research is to empirically assess this issue by specifying a cost-side function which can then be used to calculate social welfare in different scenarios. Empirical evidence on the optimal number of radio stations can be obtained by comparing social welfare for different number of radio stations. Although consumer surplus cannot be calculated in monetary terms because listeners do not pay an explicit price to listen to radio it can be compared in units. A dynamic model can also be estimated to take into account that market shares and advertising prices depend on previous values. Kaiser (2003) is the only paper that we are aware of that also estimates a dynamic model of a nested logit specification. Furthermore future research could employ a two-level nested logit to better represent the choice of listeners. The choice of listeners then becomes threefold. First, listeners decide whether or no to listen to radio, then they decide to which nest (format), and finally they choose a radio station to listen to. This model includes the substitution parameter for nests and an additional substitution parameter which measures the correlation among radio stations (Richards, 2007).
The digitalization of the FM band also raises some interesting issues for future research. Frequencies need to be assigned and therefore some pricing mechanism is required to establish a based price for each license. The best known market-based pricing tool that has been used by several European radiocommunications agencies is the auction. An auction provides an efficient way of assigning licenses as it assigns the spectrum to the bidder who values it the most. However Janssen & Moldovanu (2002) show that an auction can produce outcomes that are inconsistent with the goals of the government. If there is a market after the auction then bidders do not only care about the object but their valuations also depend who else wins in
the auction. This implies that firms take into account the expectation about future market scenarios and act strategically to achieve the best possible market structure from their perspective. A good example is given by Borgers & van Damme (2004) where the government auctions a second license to operate in a market in which already one player is active. For the potential entrant this license means a right to compete whereas the license means an opportunity to maintain the monopoly position for the incumbent. The auction will most likely allocate the license to the monopolists as his profit loss is greater then the profit gains of the entrant. It can therefore be interesting to examine pricing mechanisms that are consistent with market efficiency and take into account that the license acts as an entry ticket for the market.
Finally, there are switching costs for listeners associated with the introduction of digital radio because the analogue radio receivers are not compatible for receiving digital radio signals. So radio stations do not have an incentive to broadcast their programs digitally when there are no listeners than can receive these radio signals. On the other hand, listeners will not buy a new digital radio receiver when there are no radio stations broadcasting digitally. This is an interesting issue which might be examined in more detail.
9
Conclusion
The main purpose of this study is to examine the demand side of the radio broad-casting industry in The Netherlands with a special interest on the degree of market expansion or business-stealing. The nested logit model is well suited for this purpose because it includes the substitution parameter σ that measures this effect. Moreover the nested logit specification allows radio stations to be closer correlated with radio station that are in the same group than with radio stations from another group. The two-sidedness of the radio broadcasting market is taken into account by defining a listener demand function and an ad demand function.
of business-stealing. The entrants attracts most of its listener from the radio station that is in the same group and also some listeners from the other nests.
From a policy-making perspective, the main conclusion is that more is not always preferred to less. The (potential) increase in the number of radio stations is mostly seen as a great advantage of digital radio by offering listeners more choice. However the utility of listeners will only increase if the increased number of radio stations also results in increased variety. Otherwise the additional costs of the new radio stations do not outweigh the additional benefits of listeners and entry creates a negative externality.
Acknowledgements
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Appendix A
This appendix shows that different assumptions about the functional form of listener heteregoneity leads to different substitution patterns. The utility that a potential listener i obtains from radio station is given by:
uij = δj+ ij
where δj is the mean utility from listening to radio station j and ij is the
stochastic component of utility. A listener is assumed to listen to the radio station that maximizes his or her utility. That is, conditional on the characteristcs of a radio station, (x, ), listener i will choose to listen to radio station j if and only if
uij > uik∀j 6= k
The probability that an individual i chooses radio station j is Pij = P rob(uij > uik∀j 6= k) = P rob(δj + ij > δk+ ik∀j 6= k) = P rob(ik− ij < +δj− δk∀j 6= k) = Z I(ik− ij < δj − δk∀j 6= k)f (i)di
where I is an indicator function equal to 1 if the expression is true and 0 otherwise and f (i) is the joint density of the unobserved terms of the utility function. The
asssumption that are made about the distribution of i lead to different discrete
choice models. The logit model is obtained by assuming that i is distributed iid
extreme value whereas the nested logit model is obtained by assuming that i is a
Appendix B
Table B. Description of variables and data sources
Name Description Source
Advertising price Price for an advertising spot www.radionieuws.nl
of 20 seconds Individual websites of radio stations Contacting radio stations
Amount of Number of advertisements based on www.radionieuws.nl advertising an average length of 20 seconds Nielsen Media Research
Listen share (sj) AQH-rating of radio station j. Intomart GfK
Total listeners of radio station j divided by the population
Share of the The number of not-listeners Intomart GfK outside option (s0) divided by the population
Within-group Listeners of radio station j Intomart GfK listen share (sj|g) divided by the total listeners
Economic climate Variable measuring the economic CBS situation. This index is based on
the economic situation over the past 12 months and the future 12 months on a scale of -10 to +10
North, East, South Dummy variables equal to 1 if the if the radio station is located in the North, East or South
National Dummy variable equal to 1 if the Agentschap Telecom radio station is a national station
Public Dummy variable equal to 1 if the Agentschap Telecom radio station is a public station
Pop, Rock, News, Dummy variable equal to 1 if the www.rab.fm & 70’s, 80’s & 90’s radio station has a pop, rock, news,
or 70’s, 80’s & 90’s format
