Economics of Music Charts:
Market Concentration and Product Variety
Joanna Syrda∗†
Abstract
In entertainment markets an increasingly large number of products is introduced each
year, however only a small group achieves mainstream success and among these the distribution of market success is becoming increasingly skewed. This paper offers a supply-side
explanation based on a logit framework and shows that when multi-product firms engage
in non-price competition the distribution of optimal advertising spending is skewed towards
few most promising products on a firm level and what follows, depending on the number of
firms, on a market level. The proposed model implies that as the market becomes more concentrated the number of products released will decrease and the market shares distribution
will become more skewed. This are precisely the dynamics observed in US Recorded Music
Industry in the years 1959-1999. The model is estimated using Billboard Chart data from
that period.
JEL Classification: L11, L13, M37
Keywords: music industry, multi-product firms, advertising, market structure, variety
∗
University of Bath, School of Management. Email: [email protected]. Tel.: +44 (0) 1225 383151
I am grateful to my advisors Kenneth Hendricks, Alan Sorensen, and Amit Gandhi for helpful discussions
and comments.
†
1
1
Introduction
In entertainment markets such as the music industry there is a multitude of products introduced each year but only few achieve mainstream success and among these the distribution
of returns is extremely skewed, that is a large share of industry sales and profits is appropriated by a small number of products. Literature on the subject has thus far focused on
the demand side. One explanation is that the skewness in returns simply reflects differences
in quality of artists. Rosen (1981) laying ground for superstar literature argued that even
small differences in talent among performers are magnified into large returns differentials, as
poorer quality is only an imperfect substitute for higher quality. Thus, people prefer consuming fewer high-quality services to more of the same service at moderate quality levels.
Another demand-side explanation of superstar products is provided first by Adler (1985) who
suggested a cognitive and social form of network externalities. Marginal utility of consuming
a superstar service increases with the ability to appreciate it, which depends not only on
the star’s talent, but also on consumption capital accumulated through past consumption
activities or by discussing the star’s performance with likewise knowledgeable individuals.
The latter effect gives rise to positive network externalities. The more popular the artist
is, the easier it becomes to find other fans. Moreover, economies of search costs imply that
consumers are better off patronizing the most popular star as long as others are not perceived
to be clearly superior. Finally in the observational and social learning literature consumers
learn about products and their preferences for them from the purchasing decisions of other
consumers or from their own costly search, which may result in consumers herding in the
demand on few products as they copy their predecessors simply because it is an equilibrium
learning outcome.
However based on Billboard Music Chart data the degree of chart performance distribution skewness has not been constant over time and importantly more songs made it to
Billboard Top 100 in the 1960s and 1970s than in the 1980s and 1990s and the distribution
of chart success became significantly more skewed over time. Unless quality differentials
increased over time or there was a change in technology that supports stronger network
externalities the demand-side explanations fail to fully capture the increasing skewness of
returns in the music industry.
This key contribution of this paper is a supply side explanation. Entertainment industries display significant skewness not only of market shares but also of resource allocation.
Products like films, books, or music are essentially priced uniformly or vary within a narrow
range. The basic commercial approach of attracting customers with lower prices doesn’t apply. According to Elberse (2014) leading television networks, film studios, book publishers,
2
Year
2011
2009
2007
Table 1: Itunes sales distribution
Number of unique tracks sold [M] <100 Units
8
94%
6.4
93%
3.9
91%
1 copy
32%
27%
24%
music labels, video game publishers, and producers in other sectors of the entertainment
industry thrive nowadays on making huge investment to market concepts with strong hit potential, and they bank on the sales of those to make up for the middling performance of their
other content. Rather then spreading resources evenly across product lines and vigorously
trying to save cost in the effort to increase profits, betting heavily on likely blockbusters and
spending considerably less on the ”also rans” seems to be the way among major players to
lasting success in show business. For instance at Warner Bros. the top 10% of films produced
from 2007 to 2011 accounted for a third of costs and more than 40% of revenues.
Even as internet lowered the search cost, the shelf cost and distribution cost associated
with entertainment products, the resulting distribution of sales does not reflect the expected
rise in product variety. As Table 1 shows in the music industry of the 8 million unique digital
tracks produced and released in 2011, 94%, which amounts to 7.5 million tracks, sold fewer
than 100 units, and an astonishing 32% sold only one copy. On the other hand 102 music
tracks, 0.001% of the eight million tracks on offer that year, sold more than a million units,
representing 15% of total music sales.
In 2004 Chris Anderson introduced a quickly popularized theory of the Long Tail, arguing
that our culture and economy is increasingly shifting away from a focus on a relatively small
number of ”hits” (mainstream products and markets) at the head of the demand curve and
toward a huge number of niches in the tail. As the costs of production and distribution fall,
especially online, there is now less need to lump products and consumers into one-size-fits-all
containers. In an era without the constraints of physical shelf space and other bottlenecks
of distribution, narrowly-targeted goods and services can be as economically attractive as
mainstream fare. The distribution and inventory costs of businesses successfully applying
this strategy allow them to realize significant profit out of selling small volumes of hard-tofind items to many customers instead of only selling large volumes of a reduced number of
popular items. The total sales of this large number of ”non-hit items” is called ”the long
tail”.
However data shows that the trend is the opposite of what Chris Anderson predicted,
the recorded-music tail is getting thinner and thinner over time. Based on Itunes sales data,
two years earlier, in 2009, 6.4 million unique tracks were sold, of those, 93% sold fewer than
3
100 copies, and 27% sold only one copy. Two years earlier still, of the 3.9 million tracks that
were sold, 91% sold fewer than 100 units, and 24% sold only one copy. Although the market
for digital tracks grows, the share of titles that sell very few copies is growing as well.
The same trend can be observed in the US recorded music industry since the first time
aggregate data in any form was collected, that is when Billboard compiled all of its regional
playlists in 1959, forming a Billboard Top 100 Chart, a dataset used in this paper. As music
market grew and market concentration increased, the distribution if chart performance became increasingly skewed, with fewer and fewer songs appropriating an increasingly larger
share of chart success. Although this trend continued into the digital music era, empirical
application in this paper ends in 1999 with the introduction of Napster, marking the beginning of online music and potentially changing the way we learn and purchase music to the
point where the chart data prior and after 1999 is not comparable.
The theoretical framework is based on a logit model of product differentiation. Record
labels engage in a non-price competition, as prices in the music industry vary within a narrow
margin and arguably small differences in product prices do not drive consumer demand. Instead record labels perfectly observe song’s quality and choose optimal advertising allocation
across its pool of products.
I find that firms’ optimal allocation of advertising spending decreases exponentially in
product quality. Firms internalize demand linkages between their products, and therefore the
resulting distribution of advertising and market shares is skewed towards few most promising
products. The details are provided in the theory section, however already intuitively we can
predict that if the distribution of market shares is skewed on the record label level, as the
market becomes more concentrated and more products are released by the same firm, the
resulting distribution of market shares on the industry level will become more skewed.
This paper examines supply angles that previous work has not looked at. While there
is a rich literature on how various market structures may impact product variety, to the
best of my knowledge there is no theoretical or empirical research explicitly modeling the
impact of market concentration on the skewness of market shares distribution through the
advertising allocation mechanism. The contribution of this work is both, theoretical and
empirical. Theoretically, I propose a framework that allows to analyze multi-product firm
behavior when firms engage in a non-price competition and compete in advertising spending
allocated across a set of products with heterogeneous quality. What sets this framework
apart is, in addition to providing a supply-side explanation for the skewed distribution of
market shares observed especially frequently in the entertainment industries, such as music,
movies, and books, it also explicitly models skewed allocation of advertising spending (or
4
other resources that do not impact product quality), which is also a feature of these markets.
This approach is applicable to a number of markets.
2
Data and US Recorded Music Industry 1959-1999
2.1
Changes in the Industry
The music recording industry is over one hundred years old. The industry’s market structure
has ranged from quasi-monopoly to a textbook-like version of perfect (monopolistic) competition. In its infancy (1890-1900), much of the industry’s output was produced by a small
handful of firms. The industry experienced significant changes in market structure when new
entrants gained substantial market share, in particular during the 1920s and 1950s. However, after these brief bursts of new entry and heightened levels of competition, the industry
experienced renewed periods of high concentration. Already in 1990s, six large multinational
firms accounted for more than 95% of the industry’s output when measured at the distributor
level.
In 1948-1955, the record music industry was dominated by four firms, RCA Victor,
Columbia, Decca, and Capitol. The market concentration ratio in the record industry was
surprisingly high, especially considering that the final product, a 45 RPM record, did not
require high manufacturing spendings. Around 1956 there was a growth in competition, as
oligopolists’ control of record merchandising and distribution has been undermined due to
U.S. Supreme Court’s decree in an antitrust case and the independents were able to establish
a substantial market position.
The 1970s seem to mark the period of re-concentration. There was not only an increase in
the market shares of the leading firms, but far fewer firms were able to successfully compete
in the popular music market. As noted before, one of key factors allowing for market control
in the early era of market concentration was merchandising. In the 1970s independents were
prevented from successfully competing in the market because the total cost of promotion was
prohibitively high. Bream (1971) estimated that promotion expenses accounted for 44% of
the cost of marketing an LP record. President of the Dot records division of ABC records
said in 1973 that he would not launch an independent record company without a promotional
budget of one million dollars. According to Time RCA has spent half that amount solely
on promotion of David Bowie. The industry was approaching a similar structure to the one
of 1948. The concentration trend only accelerated over time. Tables 4 and 5 in Appendix
C show major and medium-sized mergers and acquisitions in the recorded music industry,
where most of the former ones happened in the 1980s and 1990s.
5
Figure 1: U.S. Recorded Music Industry Dynamics 1959-1999
(a) Total number of songs, artists and debuts
(b) Recorded Music Sales [100m]
15000
800
600
10000
400
200
5000
0
1960
1970
Songs
1980
1990
Artists
2000
0
Debuts
1960
1970
1980
1990
2000
(d) Concentration ratios
(c) Major vs Independent Labels
800
1
.8
600
.6
400
.4
200
.2
1960
1970
1980
Non Indie Labels
1990
2000
1960
All songs
1970
1980
CR4
1990
2000
CR6
These trends are reflected in Billboards Top 100 charts. Figure 1 shows increasing 4 firm
and 6 firm concentration ratios and that increasing market concentration is led by major
labels pushing out or acquiring independent labels.
2.2
Billboard Top 100 Chart Data
The Billboard Hot 100 chart has historically been considered the definitive list of popular
music. Pop Annuals report songs rankings on Billboard’s Hot 100 chart which is compiled
from playlists reported by radio stations and surveys of retail sales outlets. For each song I
observe the week it entered the chart and it’s initial position and the ranking in the following
weeks until the song leaves the chart. Approximately 300-750 songs enter these charts per
year which amounts to 20,440 observations in total. I also observe the record label that
released the song and I have expanded the dataset the include the parent label, where I have
taken into account all vertical and horizontal ties, that is parent label stands for the highest
level of ownership.
6
As Internet changed how consumers learn about and purchase music I use the data series
starting in 1959, when Billboard first compiled all of their regional playlists and the four
major’s firms market share amounted to 36% and ending in 1999 with the introduction of
Napster and six large multinational firms accounted for more than 95% of the industry’s
output when measured at the distributor level.
Figure 1 presents and overview of the U.S. Recorded Music Industry in years 19591999. Panel (a) shows the total number of songs, unique artist and debut that charted on
Billboard Top 100 in a given year, where debuts are verified first releases by an artists. Panel
(b) presents growing sales in real terms, therefore decreasing number of products cannot be
due to a shrinking market. Instead in the next section I argue that it is the increasing
market concentration presented in panel (c) and (d) that is the driving the increasingly
skewed product market shares distribution. Panel (d) shows growing market concentration
measured as the sum of market shares won by 4 and 6 top record labels. Panel (c) explains
that this is largely due to independent labels, ones that operate without the funding of
or outside major record labels, exiting the market or not releasing successful mainstream
products anymore. Specifically the number of songs released by major labels that make it
to top 100 decreases slowly over time while the number of successful songs produced by
independent labels has dropped significantly to almost none in 1990s.
2.3
Chart Performance and Chart Shares
As the distribution of product shares is of key interest, Top 100 Chart song data need to be
mapped into chart shares data. In the Billboard dataset every song is described by a rank
for every week the song charted. Using this information I apply a simple point system which
assigns 100 points for rank r = 1, 99 points for r = 2, and so forth, and finally 1 point for
r = 100. Chart performance of song j released by record label i is simply a sum of these
P
t
points over all the weeks a song has charted Pij = t (101 − rij
), where r denotes rank and
t denotes a week. The linear point system is a rather conservative mapping, that is due to
log-concavity of order statistics this provides a lower bound on chart performance skewness.
P
Alternative mappings such as Pij = t r1t would simply amplify the distribution skewness
ij
reported below. Song’s shares are defined as a ratio of song’s performance over the sum of
Pij
P P
and record label i0 s share
P
in the market equal to the sum of all label’s song’s shares Si = j Sij .
performance of all songs charted in a given year, Sij =
i
j
Pij
Figure 2 shows the distribution of computed song chart performance ranked from highest
to lowest. For clarity of presentation the distributions are pooled over decades, however the
decade-to-decade trend is equally pronounced year-to-year. Over time the distribution of
7
chart performance becomes extremely skewed, that is fewer songs appropriate increasingly
larger share of chart success. In the 1960s with the exception of few outliers, the distribution
of chart performance was relatively flat compared to the 1990s where a small group of songs
won a very large share of chart’s success.
Figure 2: Chart Performance Distribution
1960
1970
1980
1990
6000
4000
2000
0
6000
4000
2000
0
0
3
2000
4000
6000
8000 0
2000
4000
6000
8000
A Model of Advertising
Using the structure of the logit discrete choice model, I develop a theoretical framework
of firm’s (record label’s) optimal advertising allocation across multiple products (songs).
Record labels engage in a non-price competition, as prices in the music industry vary within
a narrow margin and arguably small differences in product prices do not drive consumer
demand. Instead record labels perfectly observe song’s quality and choose optimal advertising
allocation across its pool of songs defined by unidimensional quality.
8
3.1
Consumers
Song j is characterized by quality θj . Consumer m’s utility under the assumed non-price
competition should she buy song j is given by:
umj = θj + φ ln(aj ) + εmj
(1)
where aj is the level of advertising and φ is the parameter of advertising effectiveness.
Consumers derive utility from song’s quality and from exposure or information stock delivered
though advertising. There are diminishing returns to advertising and as limaj →0+ ln(aj ) =
−∞, consumer will never purchase a product she has never heard of as it was never advertised.
Error terms εmj are assumed to be independently and identically extreme value distributed across songs and consumers. Consumers are faced with an outside good um0 = εm0 ,
which in this context can be thought of as buying a not newly released song or not buying
a song at all.
3.2
Firm (Record Label)
Θi = {θi,1 , θi,2 , θi,3 , ....} is an exogenous set of of songs from which label i chooses the subset
of songs to release. Assume labels perfectly observe song quality and therefore are able to
rank songs so that θi,n ≥ θi,n+1 . A release of each song is associated with a fixed cost F > 0.
Moreover note that as limaj →0+ ln(aj ) = −∞ every released song will have a positive level
of advertising. Inuitively, as in consideration set theory, consumer cannot buy a song she
has never heard of. Therefore a release of song j by label i s equivalent with a positive
advertising level aij > 0.
Label i chooses a subset of songs {θi,1 , ..., θi,ni } ∈ Θi and a positive level of advertising
aij of each song so as to solve profit maximizing problem:
max M (p − c)
{aij ,ni }
ni
X
Sij −
j
ni
X
aij − ni F
(2)
j
where M is the size of the market or the number of consumers and c is the marginal cost.
As labels engage in a non-price competition price p is taken as given throughout the model.
Market shares are of the standard formulation:
Sij =
1+
e(θij +φ ln(aij ))
P
P (θ +φ ln(a ))
(θij +φ ln(aij ) +
kj
kj
je
k6=i
je
P
(3)
For further analysis song j’s market share derivatives with respect to own advertising
are equal to
∂Sj
∂aj
=
φ
aj Sj (1
− Sj ), and market share of song l, where l 6= j, with respect to
9
advertising spent on song j is given by
∂Sl
∂aj
= − aφj Sj Sl . Own advertising elasticity is given
by:
ηj =
∂Sj aj
= φ(1 − Sj )
∂aj Sj
(4)
Intuitively there are diminishing returns to advertising, as additional advertising becomes
less effective once the most responsive buyers are already reached. Cross-elasticity is given
by:
ηlj =
∂Sl aj
= −φSj
∂aj Sl
(5)
and as in any homogeneous logit specification does not depend on song’s respective characteristics or similarities between them. Independence of Irrelevant Alternatives (IIA) property
is often an undesirable assumption about how decision makers substitute among alternatives.
Specifically, IIA implies that demand must be drawn from competing alternatives in proportion to their market shares. However in this context where consumers neither differentiate
between record labels which release the songs, nor is there a significant heterogeneity in
the music genres that appear on Billboard Top 100 Charts there is no reason to impose an
alternative substitution pattern between the songs.
Throughout the paper I assume φ < 1. Should φ > 1 second order condition for an
optimal level of advertising is not always satisfied (see Appendix A). Furthermore if φ > 1
every record label would find it optimal to promote a single song defined by highest quality
(see Appendix B) which is not what is observed in the music industry or other entertainment
markets.
3.3
Step 1: Optimal Allocation of Advertising Spending across
Songs on a Firm Level
One of the central questions this paper aims to answer is how do record labels allocate
advertising spending across songs defined by quality θj and how does the resulting market
shares distribution change with market structure.
Proposition 1. Label i will allocate advertising spending across two songs defined by quality
θi1 and θi2 according to the following equation:
ln(ai2 ) = ln(ai1 ) +
θi2 − θi1
1−φ
(6)
Proof. This relationship stems directly from label i0 s first order conditions with respect to
10
S
ij
advertising spending on any individual song j given by φ(p − c)M aij
(1 −
these equal for songs j = 1, 2 leads to the following relationship
exp (θi1 +φ ln(ai1 ))
ai1
=
exp (θi2 +φ ln(ai2 ))
ai2
Si1
ai1
=
P
Si2
ai2
j
Sij ) = 1 Setting
and specifically to
and equation 6 follows.
Note that as φ < 1 a label will always invest more in the song defined by higher quality.
−θi1
Assume θi1 > θi2 . Advertising spending on song 2 is given by ai2 = exp( θi21−φ
)ai1 , i.e. it
−θi1
) ∈ (0, 1) of advertising spent on song 1. The difference in advertising
is a share exp( θi21−φ
allocated to the two songs increases exponentially in the quality differential. Moreover,
holding song’s quality θ constant, higher advertising effectiveness parameter φ implies greater
dispersion of adversing allocation across songs as lower quality songs receive a very small
share of advertising allocated to the top song. This is a result of a firm internalizing the
demand linkages between it products.
−θi1
Market shares follow pairwise the same relationship as advertising spending Si2 = exp( θi21−φ
)Si1
and same properties apply. For further analysis assume θiH to be the highest quality song
available to firm i, SiH denotes the market share of that song, and aiH denotes advertising
spending on that song. Then, using the relationship introduced by equation 6, advertising
and market shares of all other songs released by the record label can be expressed in terms
of advertising and market share of its highest quality song.
Assume the songs released by a label are ranked so that θiH = θi1 ≥ θi2 ≥ ... ≥ θin . In
order to simplify label’s optimization problem I introduce γi (θi1 , ..., θin ) which together with
θiH acts as a sufficient statistic for all songs defined by θij a label chooses to release.
γi (θi1 , ..., θin ) ≡ 1 + exp(
θi2 − θiH
θin − θiH
) + ... + exp(
)
1−φ
1−φ
(7)
P
Now total advertising spending on songs promoted by a label can be expressed as j aij =
P
γi aiH and the sum of the respective market shares can be rewritten as j Sij = γi SiH .
3.4
Step 2: Firm’s Optimal Advertising Level
Incorporating the relationships established in the previous subsection, the new profit function
is given by:
πi = (p − c)M γi SiH − γi aiH − ni F
(8)
and now finding optimal allocation of advertising spending across songs is simplified to
finding optimal advertising of the top song aiH . Market share of the top song can be rewritten
as SiH =
e(θiH +φ ln(aiH ))
P
P
1+γi e(θiH +φ ln(aiH )) + k6=i j e(θkj +φ ln(akj ))
11
and market share derivative with respect
to advertising is equal to
∂SiH
∂aiH
=
φ
aiH SiH (1
− γi SiH ). Solving the first order condition with
respect to advertising:
∂πi
SiH
= φ(p − c)M
(1 − γi SiH ) − 1
∂aiH
aiH
(9)
gives optimal advertising if the top song:
aiH = φ(p − c)M SiH (1 − γi SiH )
(10)
Intuitively incentives to advertise increase with the size and value of the market, and
effectiveness of that spending φ. Moreover the term (1 − γi SiH ) captures how firms are
internalizing the demand linkages between their products. The more products the firm
chooses to release, the less it’s going to spend on advertising on the top or any other particular
song. The total advertising spent by a record label is equal to:
γi aiH = φ(p − c)M γi SiH (1 − γi SiH )
3.5
(11)
Market Concentration, Aggregate Advertising Level, and Total
Number of Songs Released
Proposition 2. Aggregate advertising in the market decreases as market becomes more concentrated.
X
γi aiH = φ(p − c)M (1 − S0 − HHI)
(12)
i
where S0 is the share of the outside good and HHI is the Herfindahl-Hirschman Index.
Proof. The proof is straightforward. Aggregate advertising in the market is simply the sum
of individual advertising levels spent by all record labels. Using equation 11 and summing
P
P
2
over all firms in the market amounts to i γi aiH = φ(p − c)M i (γi SiH − γi2 SiH
). The
P 2 2
non-normalized Herfindahl - Hirschman Index in this context is given by HHI = i γi SiH
P
and i γi SiH + S0 = 1. Equation 12 follows.
Intuitively, the fewer firms in the market, the more one individual firm will spend on
advertising. However in aggregate the total advertising in the market will decrease as the
number of firms in the market decreases. The same logic can be extended regarding the number of products released. Holding everything else constant, the higher the total advertising
chosen by a firm, the more song will be released. Therefore the aggregate number of products behaves like total advertising level, decreasing in market concentration and increasing
12
in market size.
3.6
A Monopoly Case
For a monopolist equation 10 becomes:
aH
exp(θH + φ ln(aH ))
γ exp(θH + φ ln(aH ))
= φ(p − c)M
1−
(1 + γ exp(θH + φ ln(aH )))
(1 + γ exp(θH + φ ln(aH )))
aH = φ(p − c)M
exp(θH + φ ln(aH ))
(1 + γ exp(θH + φ ln(aH )))2
(13)
(14)
Solving this leads a a non-linear relationship:
a1−φ
H
+ 2γaH + γ 2 exp(θH )a1+φ
= φ(p − c)M
H
exp(θH )
(15)
Advertising as a function of θH has a single maximum and for large enough (p − c)M is
strictly decreasing for θH > 0 . Intuitively a monopolist is only competing with the outside
good, hence the higher the θH the less advertising is required. However for a small enough
market, returns on advertising may be low, increasingly significantly with higher song quality.
The level of advertising spent on the top song aH is increasing in the size of the market and
decreasing in γ.
3.7
A Duopoly Case
For notational clarity as there are only two firms i = 1, 2 and all variables are expressed
in terms of the top song, for each firm subscript H is dropped, therefore S1 denotes first
firm’s market share of the highest quality song defined by θ1 and promoted with advertising
spending a1 , and S2 denotes second firm’s market share of the highest quality song defined
by θ2 and promoted with advertising spending a2 . First order conditions for both firms are
as follows:
φ(p − c)M
S1
(1 − γ1 S1 ) = 1
a1
(16)
φ(p − c)M
S2
(1 − γ2 S2 ) = 1
a2
(17)
Setting these equal gives:
13
S1
S2
(1 − γ1 S1 ) =
(1 − γ2 S2 )
a1
a2
(18)
and recall market share of top song for firm 1 is given by:
S1 =
exp(θ1 + φ ln(a1 ))
(1 + γ1 exp(θ1 + φ ln(a1 )) + γ2 exp(θ2 + φ ln(a2 )))
(19)
and analogously S2 for firm 2. Assume first that each record label promotes only one
song. Having assumed γ1 = γ2 = 1 and canceling out the market share denominator, the
above equation becomes:
exp(θ1 + φ ln(a1 ))
exp(θ2 + φ ln(a2 ))
(1 + exp(θ2 + φ ln(a2 ))) =
(1 + exp(θ1 + φ ln(a1 ))) (20)
a1
a2
This simplifies to:
a1−φ
a1−φ
1
2
+
a
=
+ a2
1
eθ 1
eθ 2
(21)
Firm with a higher θ will spend more on advertising of the top song. Assuming labels
release and promote more than one song the above equation becomes:
a1−φ
a1−φ
1
2
+
γ
a
=
+ γ2 a2
1
1
eθ 1
eθ 2
(22)
Intuitively, the higher γi (either more or higher quality songs or both) the less a record
label is going to spend on its top song compared to the competitor.
3.8
Distribution Skewness of Advertising Spending Across Songs
on the Market Level: Monopoly vs Duopoly
This subsection examines in greater detail how does relative advertising allocation compare
depending on the market structure, specifically monopoly (for clarity denoted with superscript M ) and duopoly (denoted with superscripts D1 and D2 ). First, assume there are only
2 songs released, defined by θ1 > θ2 and under duopoly firm 1 releases song 1 and firm 2
releases song 2.
Proposition 3. The relative difference in advertising spending on 2 songs defined by θ1 > θ2 ,
is greater under a monopoly than a duopoly,
aD2
aM
2
> 2M
D1
a1
a1
14
(23)
Proof. A monopolist firm will simply allocate advertising spending according to aM
=
2
2 −θ1
exp( θ1−φ
)aM
1 , therefore
aM
2
aM
1
2 −θ1
= exp( θ1−φ
). Previous subsection established the equilibrium
relationship between the advertising spent on song 1 and 2 under duopoly to be:
2 1−φ
1 1−φ
(aD
(aD
D1
2 )
1 )
2
=
+
a
+ aD
1
2
eθ1
eθ 2
(24)
1
2
As the relationship between aD
and aD
in non-linear so to facilitate comparison of
1
2
relative advertising spent on song 1 and 2 under monopoly and duopoly, assume for now
D1
2
aD
2 = Ka1 , where K ∈ (0, 1) is a constant. This is not to say that the relationship is linear
but simply to establish threshold conditions. Substituting into equation 24:
1
(1 − K)aD
1 =
(aD1 )φ =
1 1−φ
1 1−φ
(KaD
(aD
1 )
1 )
−
θ
2
e
eθ1
1
1−K
(K)1−φ
1
− θ1
eθ 2
e
(25)
(26)
As φ ∈ (0, 1) and a > 0 (otherwise a record label would simply not release the product),
the LHS is necessarily positive. For the RHS to be positive, the following inequality needs
to hold:
(K)1−φ
1
− θ1 > 0
θ
2
e
e
(27)
θ2 − θ1
)
1−φ
(28)
which implies:
K > exp(
and therefore:
a2D2
θ2 − θ1
aM
2
=
K
>
exp(
)
=
1−φ
aM
a1D1
1
(29)
Advertising spending on song 2 is a greater fraction of advertising spent on song 1 under
duopoly than monopoly, in other words relative advertising allocation is more skewed under
a monopoly than a duopoly.
This can be extended to a larger number of products. Assume there are 4 songs defined
by θ1 > θ2 > θ3 > θ4 . The monopoly advertising allocation is straightforward:
aM
2 −θ1
exp( θ1−φ
), a3M
1
=
3 −θ1
exp( θ1−φ
)
and
aM
4
aM
1
=
4 −θ1
exp( θ1−φ
).
In the duopoly setting there are 3 cases to consider:
1. D1 : {θ1 , θ3 } and D2 : {θ2 , θ4 }
15
aM
2
aM
1
=
2. D1 : {θ1 , θ2 } and D2 : {θ3 , θ4 }
3. D1 : {θ1 , θ4 } and D2 : {θ2 , θ3 }
In the first case:
1 1−φ
2 1−φ
θ3 − θ1
θ4 − θ2
(aD
(aD
D1
1 )
2 )
2
+
1
+
exp
+
1
+
exp
a
=
aD
1
2
eθ 1
1−φ
eθ 2
1−φ
That is in this case γ1D1 = (1 + exp
θ3 −θ1
1−φ
(30)
4 −θ2
) and γ2D2 = (1 + exp θ1−φ
). Following
the same steps as before this leads to:
D1 φ
) =
(a
γ1D1
1
− Kγ2D2
(K)1−φ
1
− θ1
eθ 2
e
(31)
leading to the same result as long as the following holds:
γ1D1 − Kγ2D2 > 1
or as long as
D1
γ1
D
γ2 2
(32)
> K, which in turn, given the definition of K =
D2
a2
D1
a1
D2 D2
1
γ1D1 aD
1 > γ2 a2
, holds as long as:
(33)
that is as long as firm 1 spends more on advertising in aggregate than firm 2. Equation
1
(33) implies that aD
>
1
D
D2
γ2 2 a2
D1
γ1
. Introducing this condition into the duopoly equilibrium
relationship (22) implies:
D
D2
γ2 2 a2
D
γ1 1
eθ1
1−φ
2
+ γ2D2 aD
2 <
2 1−φ
(aD
2 )
2
+ γ2D2 aD
2
eθ 2
(34)
which becomes:
D2
γ2
1−φ
D
γ1 1
<
eθ1
γ2D2
< exp
γ1D1
exp
1
eθ 2
θ1 − θ2
1−φ
!
θ2
γ D2 < exp
1−φ 2
(35)
!
!
θ1
γ D1
1−φ 1
Recall from equation (7) the way γ is constructed and it follows that:
16
(36)
(37)
X
j
exp
θ2j
1−φ
!
<
X
j
exp
θ1j
1−φ
!
(38)
where θ2j is j th song of record label 2 and θ1j is j th song of record label 1. This implies
that under duopoly equilibrium conditions the firm who’s sum of quality exponents is higher
will spend more on advertising in total. In this case, given initial assumption θ1H = θ1,1 >
θ2,1 = θ2H equation (38) will likely hold assuming record labels face sufficiently similar quality
pool and due to exponential functional form the sum in (38) it is unlikely that second firm’s
qualities θ2j for j > 1 will compensate for the difference in top songs.
P
P
θ2j θ1j Still, it is possible that j exp 1−φ
> j exp 1−φ
. Intuitively, the difference a1 > a2 ,
due to quality difference, is magnified as firm 2 spends more on advertising in total decreasing
the amount allocated to its top song. This in turn results in more skewed advertising that
it would be under monopoly for those two songs. This is not true in general though. In that
case ∃j, k st. θ1j < θ2k . Recall that the duopoly equilibrium equation (22) holds for any
two products released by different firms. Then the above reasoning follows for that pair of
products, that is the relative advertising under duopoly is less skewed under duopoly than
monopoly,
aD1
j
aD2
k
>
aM
j
aM
k
.
To sum up, the firm with higher sum of quality exponents will spend more on advertising
in total. For all pairs of products that its song quality is higher than the competitor’s song
quality the resulting advertising is less skewed or closer in value than it would have been
under a monopolistic market structure.
Proposition 4. As long as the relative adversing for top songs such that θ1 > θ2 satisfies the
inequality
aD2
2
aD1
1
>
aM
2
aM
1
than the entire distribution will be weakly less skewed under a duopoly
than a monopoly, that is for any song defined by θ1 > θn the following inequality holds:
aD
aM
n
n
≥
aD
aM
1
1
(39)
Proof. It is straightforward that for any two songs promoted by the same duopolist firm
their advertising ratio will be equal to the one of a monopoly, as regardless of the market
structure a firm always allocates advertising spending across her products according to the
same rule:
aD1
aD2
θn − θ1
aM
n
n
n
=
=
exp(
)
=
1−φ
aD1
aD2
aM
1
1
1
(40)
Hence the weak inequality. Assume as before that θ1 > θ2 and these define top songs released
respectively by firm 1 and 2. In the duopoly setting the ratio of advertising spending on any
17
two songs released by different firms is higher than the one under a monopoly.
D2
aD2
aD2
θn − θ2
θn − θ2
θ2 − θ1
aM
n
n a2
n
=
=
exp(
)K
>
exp(
)
exp(
)
=
D1
1−φ
1−φ
1−φ
aD1
aD2
aM
1
2 a1
1
(41)
This can be easily seen using the 3 cases introduced before. Denoting ai as advertising
allocated to song defined by θi , the advertising ratios under duopoly compared to monopoly
are as follows:
1. D1 : {θ1 , θ3 } and D2 : {θ2 , θ4 }
•
aD
2
aD
1
>
aM
2
aM
1
As derived above,
•
•
aD
3
aD
1
aD
3
aD
1
aD
4
aD
1
aD
4
aD
1
=
aD
2
aD
1
aM
3
aM
1
3 −θ1
= exp( θ1−φ
)=
>
=
aM
4
aM
1
D
aD
4 a2
D
aD
2 a1
2 −θ1
= K > exp( θ1−φ
)=
aM
2
aM
1
aM
3
aM
1
4 −θ2
4 −θ2
2 −θ1
= exp( θ1−φ
)K > exp( θ1−φ
) exp( θ1−φ
)=
aM
4
aM
1
2. D1 : {θ1 , θ2 } and D2 : {θ3 , θ4 }
•
•
aD
2
aD
1
aD
2
aD
1
aD
3
aD
1
=
aM
2
aM
1
2 −θ1
= exp( θ1−φ
)=
>
aM
2
aM
1
aM
3
aM
1
Analogously to case 1,
•
aD
4
aD
1
aD
4
aD
1
>
aD
3
aD
1
3 −θ1
= K 0 > exp( θ1−φ
)=
aM
4
aM
1
4 −θ3
4 −θ3
3 −θ1
= exp( θ1−φ
)K 0 > exp( θ1−φ
) exp( θ1−φ
)=
aM
3
aM
1
aM
4
aM
1
3. D1 : {θ1 , θ4 } and D2 : {θ2 , θ3 }
•
aD
2
aD
1
>
aM
2
aM
1
Shown in case 1.
•
•
aD
3
aD
1
aD
3
aD
1
aD
4
aD
1
aD
4
aD
1
>
aM
3
aM
1
3 −θ2
2 −θ1
3 −θ2
= exp( θ1−φ
)K > exp( θ1−φ
) exp( θ1−φ
)=
=
aM
4
aM
1
4 −θ1
= exp( θ1−φ
)=
aM
3
aM
1
aM
4
aM
1
The more firms operate in the market the less skewed the advertising spending, as a
higher fraction of products belongs to 2 different firms and hence the ratio of advertising
spending behind their release is higher than when released by the same firm. This is shown
in greater detail in the next subsection.
18
The same logic applies to market shares. Note that for any two songs, the ratio of their
respective market shares is equal to:
ln
Therefore
3.9
aD2
2
aD1
1
>
aM
2
aM
1
implies
S2
S1
S2D2
S1D1
= (θ2 − θ1 ) + φ ln
>
S2M
S1M
a2
a1
(42)
and the rest follows.
Distribution Skewness of Advertising Spending Across Songs
on the Market Level: The General Case
The point made by comparing the monopoly and duopoly case can be easily generalized
using equation (42). Based on the equilibrium relationship derived in the previous section,
specifically equation (18), the ratio of advertising spendings on any two songs is given by:
a2
S2 (1 − γ2 S2 )
=
a1
S1 (1 − γ1 S1 )
(43)
where S1 and S2 are market shares of any two songs, i.e. this relationship holds whether
the songs are released by the same label or two different labels. Substituting (43) in equation
(42) gives:
ln
S2
S1
φ
θ2 − θ1
+
ln
=
1−φ
1−φ
(1 − γ2 S2 )
(1 − γ1 S1 )
(44)
Assuming θ2 > θ1 and that these are label’s top songs definitely implies S2 > S1 , and
as was also discussed before γ2 S2 > γ1 S1 , that is label with the higher quality top song
spends more on advertising of that song and in total, winning a larger market share. This
implies that
(1−γ2 S2 )
(1−γ1 S1 )
(1−γ2 S2 )
< 1 and ln( (1−γ
) < 0. On the other hand when these two songs are
1 S1 )
released by the same firm γ2 S2 = γ1 S1 , that is this represents the total market share of the
same firm, then
(1−γ2 S2 )
(1−γ1 S1 )
2 S2 )
= 1 and ln( (1−γ
(1−γ1 S1 ) ) = 0 and equation (44) is equivalent to firm’s
optimal advertising allocation relationship (6). This confirms for the general case that
S2
S1
is
higher when it is a market shares ratio of two songs released by the same record label rather
than two different labels.
This also provides the simplest intuition for why under higher market concentration
the distribution of market shares, here measured by
S2
S1 ,
is more skewed. The fewer firms
operate in the market the higher fraction of products is released by the same firm leading to
(1−γ2 S2 )
(1−γ1 S1 )
= 1 and thus leading to higher market shares ratios.
In addition to market concentration the other factor that influences market share skewness
is the size or value of the market. Recall the duopoly equilibrium equation (22),
19
a1−φ
1
+γ1 a1
eθ1
=
a1−φ
2
eθ2
+γ2 a2 . Advertising is a strategic complement, as one firm advertises more the other one
does so also. The more both firms advertise the less the difference between their respective
advertising levels a2 and a1 is determined by quality differences. Market size or value is a key
incentive to advertise, as market grows in size firms will advertise more and holding γ2 and
γ1 constant, the advertising spent on labels’ top songs, a2 and a1 , will become closer in value.
Therefore both ratios,
a2
a1
and
S2
S1
will become lower, leading to a less skewed distribution of
market shares as the market grows.
Finally, higher φ reinforces the market concentration result, as it leads to more skewed
distribution of market shares on the firm level however less skewed distribution of top songs
released by different record labels. From equation (22), the higher the φ the less important
is the quality difference in the duopoly advertising equilibrium.
4
Estimation
Equation (42) is central to the estimation strategy and holds for any two songs, whether
they are released by the same firm or two different firms.
ln
S2
S1
= (θ2 − θ1 ) + φ ln
a2
a1
(45)
where S1 and S2 are market shares of any two songs and are observed in the data as chart
shares. For the purpose of the estimation I assume these adequately approximate market
shares in the mainstream music market. Although advertising is not observed in the dataset,
based on the equation (43) introduced in the previous section:
a2
S2 (1 − γ2 S2 )
=
a1
S1 (1 − γ1 S1 )
(46)
advertising ratio can be easily computed with the use of available data. The underlying
distribution of quality F (θ) is assumed to be constant over time, and as discussed in the
next subsection, exponential. There are numerous advantages to this approach. First, it
requires a minimum of data, only individual product market shares and ties to the firms
releasing the product to compute γS, i.e. the sum of market shares of all of firm’s products.
Secondly, unlike in numerous applications of the logit framework, there is no need to make
assumptions on the market size and the share of outside good. Finally, it uses the panel
stricture of the data, specifically the information on the year-to-year changing skewness of
market shares (market shares ratios).
20
4.1
Empirical Approach
Assume that product quality θ is exponentially distributed. This approximated the generally
held belief that high quality, or superstar talent, is rare. The probability density function of
the one-parameter exponential population with scale parameter β is given by:
f (θ; β) =
1 −θ/β
e
,
β
θ ≥ 0, β > 0
(47)
The quality that defines the charted songs θ can be ranked and analyzed as order statistic
such that θ(1) < θ(2) < ... < θ(n) . From the works of Sukhatme (1937) and Epstein and Sobel
(1953), it is known that the expected value of the k th order statistic of a sample of size n
from the population is given by
E(θk,n ; β) = β
k
X
j=1
1
n−j+1
(48)
where β is the distribution mean and the lower the β the more skewed the distribution
of θ. Then the difference between two order statistics k and k − m is given by:
E(θk,n ; β) − E(θk−m,n ; β) = β
X
k
j=1
k−m
X
1
1
−
n−j+1
n−j+1
j=1
E(θk,n ; β) − E(θk−m,n ; β) = β
k
X
j=k−m+1
(49)
1
n−j+1
(50)
Therefore the estimated equation becomes
ln
St,(k)
St,(k−m)
=
β
(1 − φ)
k
X
j=k−m+1
1
φ
+
ln
n − j + 1 (1 − φ)
(1 − γt,(k) St,(k) )
(1 − γt,(k−m) St,(k−m) )
+ εtk
(51)
Large sample size warrants the use of expected values of order statistics. Equation 51
could also be estimated for market share ratio with respect to one song, for instance the one
that won the largest market share in a given year. However given the use of expected values
and for any given order statistic a maximum of 41 observations, using varying ratios appears
to be a more robust approach, as it does not tie all the estimates to one particular order
statistic.
Based on the theoretical predictions of the model, the record label with a higher quality
top song will spend more on advertising that song and win a larger market share. Therefore
in the group consisting of every firm’s song with the highest market share, the songs with
21
higher market share are defined by higher quality θ. Secondly, within a record label higher
quality songs will receive more advertising and win a larger market share. In both subgroups,
inter-label top songs and intra-label songs, relatively higher market share results from higher
quality. This however does not need to be the case for all songs. For instance it is possible
that one firm’s top song is of lower quality than another firm’s second best song however
it wins a larger market share. Still in terms of θ order statistics the ranking of market
shares provides the best approximation of quality ranking. Therefore the order statistics of
quality are constructed based on the ordering of market shares in a given year, that is k th
market share corresponds to expected value of k th order statistic of quality θ but with the
possible error of this assumption in mind, two approaches to estimation are tested. First
constructs the market shares ratios by, in one group, comparing the market shares of every
label’s top songs and in the second group, comparing market shares of songs released by the
same record label. This ensures that higher market shares results form higher quality and
the only possible error is in the size of the difference between θ order statistics. The second
approach pools all songs together and the market shares are constructed in any given year
S(n)
S(n−1)
S(n−1) , S(n−2) ,
top to bottom, i.e.
and so forth. The estimated parameter φ is found to be
similar for non-independent and major lables under both approaches, which is reassuring.
Therefore, for the first approach, in the inter-label group the market shares of top songs
are ordered so that S(i),H > S(i−1),H > ... > S(i−I),H for every year and market share
ratios are constructed as
S(i),H
S(i−1),H .
In the intra-label group, market shares are ordered so that
Si,(H) > Si,(H−1) > ... > Si,(H−ni ) for every year and market share ratios are constructed as
Si,(H)
Si,(H−1)
Si,(H−1) , Si,(H−2)
and so forth. This ensures that for the compared song through market shares
ratios, higher quality implies higher market share. While estimated using one equation, for
the two subgroups equation (51) becomes:
ln
St(i)H,(k)
St(i−i)H,(k−m)
=
β
(1 − φ)
k
X
j=k−m+1
1
φ
+
ln
n − j + 1 (1 − φ)
(1 − γt(i)H,(k) St(i)H,(k) )
+εtk
(1 − γt(i−i)H,(k−m) St(i−i)H,(k−m) )
(52)
ln
Sti(H),(k)
Sti(H−1),(k−m)
=
β
(1 − φ)
k
X
j=k−m+1
1
+ εtk
n−j+1
(53)
Equation (53) simply becomes equation (6), i.e. the relationship between label’s market
shares stemming from optimal advertising allocation.
In the pooled approach, the difference between two successive order statistics and the
estimated equation are given by:
22
Table 2: OLS Regression Results: Inter-Label Top Songs Subgroup and Intra-Label Subgroup
All Record Labels
1.285***
(.008)
1.863***
(.351)
Non-Independent Labels
1.112***
(.007)
5.171***
(.297)
Top 6 Major Labels
1.104***
(.006)
5.432***
(.263)
β
0.449
0.180
0.172
φ
0.651
0.838
0.845
N
20416
13907
R2
0.5445
0.6644
1) Standard errors in parentheses.
2) *: p < 0.10, **: p < 0.05, ***: p < 0.01
12000
0.7666
β
1−φ
φ
1−φ
E(θk,n ; β) − E(θk−1,n ; β) =
ln
S(k)
S(k−1)
=
φ
β
1
+
ln
(1 − φ) n − k + 1 (1 − φ)
β
n−k+1
(1 − γ(k) S(k) )
(1 − γ(k−1) S(k−1) )
(54)
+ εtk
(55)
Under both approaches, both the distribution mean β and advertising effectiveness parameter φ can be simultaneously estimated. Moreover the use of order statistics is particularly suitable as the number of songs charted n does not influence whether the top k songs
make it to the chart as long as n >> k. The OLS estimation results are reported in tables
2 and 3.
4.2
Results
Both estimations were carried out for the entire sample, the non-independent (major) labels
and top 6 major labels as measured in terms of total market size. The model provides a good
fit for the chart data and intuitively, R2 is higher for the non-independent record labels than
the entire sample and highest for the top 6 major labels. An independent record label is one
that operates without the funding of or outside major record labels, therefore it is explicitly
constrained in it’s optimization. The parameter φ value is in the range 0.830 − 0.856 in 5
out of 6 estimations, which implies relatively high significance of advertising and skewness
of market shares distribution in a concentrated market.
23
Table 3: OLS Regression Results: Pooled Sample
All Record Labels
.525***
(.003)
5.690***
(.297)
Non-Independent Labels
.526***
(.003)
5.930***
(.604)
Top 6 Major Labels
.507***
(.003)
4.870***
(.923)
β
0.078
0.076
0.086
φ
0.851
0.856
0.830
N
20416
13907
12000
R2
0.6390
0.6873
1) Standard errors in parentheses.
2) *: p < 0.10, **: p < 0.05, ***: p < 0.01
0.6986
β
1−φ
φ
1−φ
5
Concluding Remarks
In recent years the study of models of multi-product competition with product differentiated
variants has become an important field both for theoretical and empirical industrial organization. This is no surprise, since multi-product firms are ubiquitous, but what is noteworthy
is how difficult it is to model them satisfactorily. In this paper based on a logit framework I
introduce a model in which multi-product firms engage in non-price competition and compete
in advertising spending. A firm will internalize the demand linkages between it’s products
in a way that competing firms do not, and the relative advertising allocated to every next
product and the resulting market share is much lower than it would be if the two products
were released by two different firms. The distribution of market shares on a firm level is
highly skewed, that is for a given quality difference between two products the optimal ratio
∆θ
of allocated advertising and resulting market shares is magnified by exp( 1−φ
), where φ is
estimated to be using the Billboard Chart Data to be in the range 0.830 − 0.856. The fewer
firms operate in the market the higher fraction of songs are supported by a highly skewed
advertising allocation. In aggregate the higher the market concentration, measured using
HHI, the lower the total advertising spent by all firms in the market, leading to a lower
number of products released and more skewed distribution of market shares.
In addition to a wide array of theoretical prediction the model allows to simplify the
modeling of multi-product firm choices to a single advertising variable optimization and
24
empirically, estimation requires very little data. Given numerous highly concentrated (entertainment and cultural goods) industries, where multi-product firms compete in advertising
the proposed theoretical and empirical framework is widely applicable.
25
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Appendix A
Interior solution to the advertising prob-
lem
First profit derivative:
X
∂πi
Sij
= φ(p − c)M
(1 −
Sij ) − 1
∂aij
aij
j
(56)
Second profit derivative:
X
∂ 2 πi
Sij
= φ2 (p − c)M 2 (1 −
Sij )[φ(1 − 2Sij ) − 1]
2
∂aij
aij
j
(57)
∂ 2 πi φ(1 − 2Sij ) − 1
=
∂a2ij ∂πi /∂aij =0
aij
(58)
Second order condition is satisfied for
φ−1
2φ
≤ Sj which is always satisfied for φ < 1 as the
left hand side is negative. For φ > 1 there exists an inflection point where for a song market
share below that level profits are convex in advertising.
Appendix B
Number of songs and φ
For fixed cost F = 0 number of songs a label releases depends on the value of φ. For φ = 1 a
firm is indifferent between releasing any number of songs, for φ > 1 it is more profitable for
a firm to promote a single songs, and for φ < 1 profits are increasing in the number of songs.
For simplicity assume all songs are defined by equal quality. This implies that ∀j : aj = a
and ∀j : Sj = S. The profit function is then given by π(a; n) = (p − c)M nS − na.
First order derivative with respect to the number of artists is given by:
∂π(a; n)
= (p − c)M S(1 − nS) − a
∂n
(59)
First order derivative with respect to advertising a is given by:
∂π(a; n)
nS
= φ(p − c)M
(1 − nS) − n
∂a
a
(60)
1
∂π(a; n) = a( − 1)
∂n
φ
∂π(a;n)/∂a=0
(61)
This is positive for φ < 1 and negative for φ > 1. That is for φ > 1 a label will release
only one song defined by the highest quality.
29
Appendix C
Mergers and Acquisitions
Table 4: Major Mergers and Acquisitions in the Music Industry, 1959-1999
Year
Buyer
Target
Share [%] Real Value 100%
1962/1972 PPI
Deutsche Grammophon
Merger
n/a
1962
MCA
Decca
Merger
n/a
1986
Bertelsmann RCA
100
463
1987
Sony
CBS
100
2,732
1989
PolyGram
A&M
100
586
1989
PolyGram
Island
100
568
1990
MCA
Geffen
100
662
1992
EMI
Virgin
100
1,108
1995
Seagram
MCA
80
6,188 7,735
1998
Seagram
PolyGram
100
11,035
30
Table 5: Various Medium-Sized Acquisitions by Multinationals, 1959-1998
Year
Buyer
Target
Share [%] Real Value 100%
1962
PolyGram
Mercury
100
9
1963
Warner
Reprise
67
55
82
1966
ABC
Dunhill
100
13
1967
Warner
Atlantic
100
82
1967
PolyGram
RSO
100
33
1969
GRT
Chess
100
31
1970
Warner
Elektra
100
36
1971
Warner
Asylum
100
24
1972
CBS
Stax
100
23
1972
PolyGram
MGM
100
n/a
1976
EMI
Screen Gems
100
58
1977/1980 PolyGram
Casablanca
100
51
1979
MCA
ABC
100
81
1979
Bertelsmann Arista
100
101
1979
EMI
United Artists
100
6
1979
PolyGram
Decca
100
60
1983
RCA
Arista
50
n/a
1986
MCA
Full Moon
100
22
1988
MCA
Motown
20
81
405
1989
PolyGram
Welk
100
32
1989/1991 EMI
Chrysalis
100
179
1990
Warner
CBS
50
382
764
1993
PolyGram
Motown
100
341
1994/1997 PolyGram
Def Jam
100
139
1996
MCA
Interscope
50
213
426
31