The Impact of Price discrimination on Revenue: Evidence from the Concert Industry

Pascal Courty and Mario Pagliero1

First draft: April 2008

Abstract: Some artists sell all seats in a concert at the same price while others offer multiple seating categories. We use variations in the number of seating categories across concert, tour, artist, location, and time to estimate the relationship between price discrimination and revenue. Offering multiple seating categories increases revenue by about 5 percent. This figure increases with the level of competition, in markets where demand is more heterogeneous, and for bands that appeal to more fragmented public.

JEL: A12, D01, D12.

Keywords: Price discrimination, return, second degree price discrimination.

Copyright © Pascal Courty and Mario Pagliero

1 Pascal Courty, Department of Economics, European University Institute, Villa San Paolo, Via Della Piazzuola 43, 50133 Firenze, Italy, Pascal.Courty@IUE.it; Mario Pagliero, University of Turin, Department of Economics and Finance, 10134 Turin, Italy, and Collegio Carlo Alberto, Mario.Pagliero@unito.it.

1-Introduction

Although price discrimination receives much attention in the economic literature, there

is little empirical evidence documenting its impact on revenues. Much progress has been

achieved in recent years with the introduction of structural estimation but existing studies

focus on a single firm (Leslie, 2004) or single market (McManus, 2008). This paper

provides the first systematic non-structural evidence on the relationship between price

discrimination and revenue.

We exploit a rich panel dataset on concerts for popular music that covers a major share

of the US concert industry. We establish two contributions. First, we estimate the impact

of price discrimination on revenue. Second, we document how this impact depends on

product, seller, and market characteristics, which have been shown to matter in the theory

literature (Stole, 2007, and Anderson and Dana, 2006).

Concerts for popular music offer a unique environment to study price discrimination.

First, concert pricing is a textbook case of price discrimination. The practice of selling

tickets for similar seats at widely different prices can be unambiguously attributed to price

discrimination. This is because seating capacity is given, and the variable cost of adding an

additional seating category is small, or possibly zero. In contrast, in most of the

applications considered in the literature, one has to deal with the possibility that price

differences across products could be due to variations in marginal cost instead of price

discrimination (Shepard (1991), Clerides (2004)). Second, pricing policies in the concert

industry are relatively simple: for each venue, a pricing policy partitions the set of seats

into categories and specifies a price for each category. Our dataset covers over 21,000

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concerts by the largest 100 sellers or artists in the industry over the period 1992-2005.2

For each concert in our sample, we have information not only on pricing policies, as in past

non-structural studies of price discrimination (e.g. Shepard 19991, Nevo and Wolfram

2002, Busse and Rysman, 2004) but also on revenue, therefore allowing us to investigate

how the former influences the latter. Finally, we have collected information on product

characteristic (venue, music genre, type of tour), seller characteristics (artist and band

information), and demand (characteristics of the local population at the state and city

level).

In the core of this work, we measure price discrimination by distinguishing those

concerts that offer all seats at the same price from those that offer more than one price

category. Concerts with one or two seating categories represent the majority (71 percent)

of the concerts in our dataset. We compute the effect on revenue of splitting the venue in

multiple seating categories, relative to offering all seats at the same price.

In our baseline regression, we find that the return from using price discrimination

amounts to 5 percent of average revenue in our sample. This preliminary finding is

descriptive to the extent that the association between price discrimination and revenue can

be interpreted in various ways. Above all, artists may choose to price discriminate in

markets where it is more profitable to do so, and our estimates may pick up both a

selection effect and the causal effect of price discrimination on revenue. Other sources of

unobserved heterogeneity could also be correlated with price discriminate and revenue thus

confounding the interpretation of the baseline finding.

We pursue two empirical strategies to investigate whether the relationship we identify is

causal. First, we follow the same approach as Nevo and Wolfram (2002) and test for

2 The top 100 grossing artists represent the largest fraction of the industry. If one would expend the sample to the top 500 grossing artists over the same period, for example, the top 100 artists would represent 70 percent of total revenue.

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unobserved heterogeneity by exploiting the rich nature of our panel. The robustness of the

baseline results with a restricted set of fixed effects, to a wide set of richer specifications

suggests that taking a difference in difference in difference (product, location, time) is

sufficient to control for unobserved heterogeneity. In the absence of unobserved

heterogeneity, the observed variations in the use of price discrimination could be due to

exogenous variations in artists’ and promoters’ ability and willingness to price

discriminate. Under this scenario, the interpretation of our estimates as causal would be

correct. In fact, we present much evidence that promoters vary in their experience with

price discrimination and access to local market information.

Another alley to make progress on causality is to focus on the details of the theoretical

mechanisms through which price discrimination may influence revenue. (An approach that

has prove fruitful in other empirical fields (e.g. Rajan and Zingales, 1998).) We study how

the relationship between price discrimination and revenue varies as a function of demand

and product characteristics. Monopoly pricing theory suggests that price discrimination

should be less profitable when the population of consumers is more homogenous. This

specific prediction of the theory can be used to construct a test of the impact of price

discrimination on revenue. Among concerts in which price discrimination is used, we study

whether concerts with more heterogeneous population are more profitable. There are two

merits to this approach. First, we look for evidence of a specific causal mechanism of price

discrimination on revenues. Second, this second test only uses variability in revenues

across concerts that do use price discrimination; controlling for the fact that concerts in

which price discrimination is used may have some other unobserved characteristic which

also affects revenues. We find that heterogeneity in age, occupation, and race increase the

return to price discrimination. We also consider product characteristics. We show that the

return to price discrimination is higher for bands with a higher fraction of female members

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and older and more prominent bands. An interpretation to this result is that these bands

cater to more diverse publics. The consistency of the impact of interacted effects in this

second set of results with standard economic theory further supports the interpretation of

our initial results.

After establishing these benchmark results, we investigate two issues that are of interest

for policy makers and industrial economists. Firstly, we consider the role of competition,

measured by the average frequency of concerts offered each year in a given city. We find

that competition decreases revenue but increases the return to price discrimination. These

results are consistent with some models of price discrimination and competition (e.g. see

Stole (2007) for a review), but could also be interpreted by the fact that there is more

information available on how to segment a venue in more competitive markets; an

interpretation that we revisit when we discuss the impact of promoter’s experience on the

return to price discrimination. Our findings complement the empirical work of Verboven

(1996) and Busse and Rysman (2005). Both studies show that the extent of price

discrimination increases with market competition. In contrast, we consider the interacted

impact of price discrimination and competition on revenue.

Second, how does revenue change when the number of product categories increase? We

distinguish the impact of offering 2, 3, and 4 product categories and show that the marginal

impact of adding a seating category is decreasing with the number of seating categories.

This third set of results contributes to the literature investigating the return to complex

product portfolios (Wilson 1993, Miravete 2007).

This paper is organized as follows. The rest of this section briefly summarizes the

literature. The next section presents the concert industry, the data, and outlines some

stylized facts on why and how sellers split venues in different seating sections. Section 3

introduces the econometric framework and discusses how one should interpret the

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relationship between price discrimination and revenue. Section 4 presents the results.

Section 5 concludes and suggests implications of our results for future research.

Literature

Our evidence contributes to previous non-structural studies of price discrimination

(Shepard (1991), Nevo and Wolfram (2002), Busse and Rysman (2005)). As mentioned

earlier, an issue that has received much attention is whether the practice of selling similar

goods at different prices is due to price discrimination or to marginal cost pricing. For

example, the studies mentioned above have used a panel data approach to investigate why

firms use differentiated pricing. In contrast, this paper selects an industry where price

differentiation is due to price discrimination and investigates the relationship between price

discrimination and revenue.

This study complements the evidence from structural micro econometrics (Leslie 2004,

Miravete and Röller 2004 , McManus, 2008). It is most closely related to Leslie, and the

magnitude of our estimates is consistent with the results presented in his simulations. In

contrast with most structural work, however, our evidence comes from an entire industry.

In addition, the identification comes from variations in pricing policies rather than from

functional form assumptions as in Leslie. Finally, we propose an alternative approach to

measure how market characteristics influence the return to price discrimination.

Finally, our work contributes to the cultural economics literature. Connolly and Krueger

(2006) have documented trends in income level and income inequalities across artists over

a similar time period as ours. They show that the level of price and the use of price

discrimination have increased dramatically over the period. We control for this industry

trend, and we use variations in pricing policies to estimate the relation between price

discrimination and revenue. Finally, Rosen and Rosenfield (1997) apply the theory of

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price discrimination to ticket markets and Huntington (1993) investigates the impact of

price discrimination in a sample of thirty-three theaters that received subsidies from the

Arts Council of Great Britain.

2- Concert Tour Industry: Data, Definitions, and Stylized Facts

The modern touring industry was born in the late 1960’s when a few bands such as the

Rolling Stone and Led Zeppelin started to systematically tour in a mix of arenas and

stadiums, using their own experienced crew to take care of sound, light, and staging. The

1980’s saw much development in technology with more ambitious stage shows, louder and

brighter, that could be seen by more people at once. By 2007, the North American concert

business revenue had grown to $4billion and 100 million in attendance.3

Most concerts in our sample (19,550 concerts out of 21,131) are part of a tour. Briefly

summarized, a concert tour typically starts with an artist represented by its manager, a

(booking) agent, and a promoter. The artist and the agent agree on an act and a tour plan.

The agent then looks for promoters to organize the event in each city. The artist agrees

with each promoter on a pricing policy and on a revenue sharing rule. Promoters are in

charge of organizing the events and this involves booking venues, advertising, and

collecting revenues, among other decisions. We have the identity of the main parties

involved in organizing a concert (artists, venue, and promoter) except for the agent that

plays only a matching role.

There are some variations around this model. Some artists use a single promoter who is

in charge of the entire tour and possibly add local promoters in some cities to have access

3 The information on the touring industry presented in this section was collected by interviewing concert promoters as well as two university professors who teach courses on concert promotion. Some of the information was also drawn from recent books targeted to professionals on concert promotion, and in particular Waddell et al. (2007).

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to local expertise which is central to success as we will see soon. A few artists do

everything in-house and directly contact the venues. Although there are different types of

tours (tour that support an album, seasonal tour, festival tour), all concerts in a tour

typically include a common set of songs, a similar stage design, and are marketed together.

Therefore, one would expect the demand for two different concerts in a given tour to be

related.

Pricing and local knowledge

Ticket prices are typically decided when the tour is announced and they are not changed

afterwards.4,5 Artists and promoters, however, vary in their ability and/or willingness to

design optimal pricing policies. In fact, concert promotion involves much know-how that

is market specific. This is because artists and promoters share to a large extent the same

set of resources in each market, and this includes first and foremost the venue, but also

promotion channels such as radios and providers of ancillary services. Waddell et al.

(2007) reports that “the art of concert promotion is derived from the experience, instinct,

knowledge of the event to promote and who it appeals to, innate sense of timing, and flair

necessary to capture public awareness” (p. 193) and also comment in details about the role

of local information “we work with each promoter and try to tap into their vast knowledge

of their local market to make the right call on when to put a show on-sale, ticket scaling

options and when you roll into multiple shows” (p. 47). As a result, one would expect that

access to market information, promoter experience, and willingness to innovate influence

the decision to use of multiple seating categories.

4 Promoters may add or cancel events but they do not update prices or category allocation. 5 Courty (2003) reviews the primary and secondary markets for concert tickets.

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2-1 Data

The data comes from three sets of sources. The core of the data was collected by

Billboard and contains similar variables as the ones used by Connolly and Krueger (2006).

For each concert defined by a date, venue, and artist(s), we observe the promoter in charge,

the different prices offered, the capacity available, and the realized attendance and revenue.

In addition, we have collected tour dates from the bands’ and fans’ websites. Finally, we

have collected information regarding the bands from music websites, artist websites, and

the Rolling Stone Encyclopedia of Rock and Roll.

As a result, our panel data is three dimensional and each dimension can be aggregated at

different levels of coarseness. The first dimension describes the product, i.e. a concert, and

can be aggregated by music genre, artist, or tour. The second dimension describes the

local demand and can be aggregated at the state or city level. In addition, venue dummies

capture a mix of product and demand characteristics. Finally, the third dimension is time

and can be dummied at the month or year level.

There are several differences with the Connolly and Krueger (2006) dataset. In term of

breadth, we focus on the top 100 grossing artists over the period 1992-2005. In term of

depth, our data is richer in several dimensions. First, we observe all prices for each

concert, instead of only the high and low price. Second, we know whether a concert is part

of a tour and which type of tour. This additional information allows us to provide a much

more complete picture of the pricing strategies across seating categories that operate at the

tour level.

2-2 Definitions and stylized facts

In our sample, 56 percent of the concerts offer two price categories, 25 percent one, 15

percent three, and the other 4 percent offer four categories. We say that a seller price

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discriminates if she does not offer all tickets at the same price. This definition of price

discrimination makes a distinction between unsophisticated pricing (general admission)

and sophisticated pricing (differentiated seating). While the core of this paper focuses on

this broad distinction, section 4.5 computes the return to each additional pricing categories.

Beyond these distinctions, we do not attempt to measure the intensity of price

discrimination (Clerides, 2004).6

The use of price discrimination varies across artists, years and cities. A linear

probability model explaining the existence of price discrimination with artist, year and city

specific fixed effects accounts for 52 percent of the variability in the use of price

discrimination. This is consistent with the theoretical prediction that the return to market

segmentation should depend on product and demand characteristics. Price discrimination

should also vary across venues within the same city if venues impose physical constraints

on the ability to differentiate seating categories. In addition, we would also expect price

discrimination to vary by tour because all concerts within a tour share a similar stage

design that constrains how the venue can be split, and a set of songs, that may appeal to a

given demographic profile. A linear probability model explaining price discrimination

with venue, tour, and year fixed effects accounts for 56 percent of the variability in price

discrimination.

These two preliminary results are consistent with price discrimination not being entirely

determined by artists, location, venue, tour, and time fixed effects. In fact, these effects

explain just above half of the variability in price discrimination use. There may be other

reasons for why price discrimination is adopted. As documented earlier, price

discrimination may vary across concerts because of variations in promoter expertise,

6 For example, two pricing policies with the same number of categories and the same prices are equally classified as discriminating according to our definition, although they may allocate different fractions of seats to each category.

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turnover within promotion firms, and access to recent market information. In addition,

promoters sometimes decide to experiment or innovate with price discrimination. Waddell

et al. (2007, p. 199) report that “every once in a while you’ll have a clever promoter in

another market that comes up with an interesting idea you never thought of, and if it works

in one place it might work in another. The agent giving you that kind of information is

terrific.” The next section discusses how one should factor in these alternative rationales

for price discrimination in interpreting the relationship between price discrimination and

revenue. Finally, artists may vary in their willingness to adopt new pricing policies.

3-Empirical Framework and Interpretation

Our empirical objective is to identify the impact of price discrimination on revenue. To

achieve this objective, one would ideally want to randomly allow some sellers to price

discriminate and measure the impact of doing so on revenue. In the absence of such ideal

conditions, one may leverage the fact that the use of price discrimination may vary for

exogenous reasons. We argued earlier, for example, that artists and promoters vary in their

access to updated local market information and expertise. In practice, however, pricing

policies may also vary for endogenous reasons. The decision to price discriminate rests on

the artist and promoter who jointly organize the event. This implies that artist, promoter,

and demand and supply characteristics may potentially be correlated with the use of price

discrimination and concert revenues. If we do not account for these effects, our estimates

of the impact of price discrimination on revenues could be biased. In addition, omitted

variables could further confound the interpretation of the empirical relation between price

discrimination and revenue.

In absence of experimental data, we can make progress pursuing two approaches: (a)

rule out many sources of unobserved heterogeneity, (b) test theoretical prediction on how

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the relation between price discrimination and revenue should depend on market

characteristics. The specifications we estimate are derived from the following general

model,

ln(Ri) = γ0+ γ’tour + γ’venue +γ’artist-city+ γ’artist-year+ γ’city-year + γ’artist-promoter+

+Xiγ1+ PDi [γ2 +Yiγ3]+ εi (1)

where Ri is the revenue in concert i; the variables γ’ are fixed effects, Xi is a vector of

concert characteristics, such as venue capacity and number of artist performing, affecting

revenues for concert i; PDi is an indicator variable that is equal to one if more than one

price category is offered, zero otherwise; Yi is a vector of concert characteristics, such as

music genre and band characteristics, affecting the return to price discrimination (a full list

of the variables included in Xi and Yi is presented in Table 3). γ0 and γ2 are scalars and γ1,

and γ3 are vectors of parameters to be estimated. εi is and error term.

We estimate by ordinary least squares different variations of model (1). The simplest

specification with no fixed effect and γ3=0 measures the association between price

discrimination and revenue. Specifications with fixed effects allow us to control for

unobserved heterogeneity. Specifications with interaction variables Yi allow us to

investigate predictions of the theory on the relative impact of price discrimination holding

the decision to price discriminate constant.

Unobserved Heterogeneity

We control for supply and demand characteristics using observable concert

characteristics and a rich set of fixed effects to allow for unobserved heterogeneity, and

demonstrate the stability of the results (Nevo and Wolfram, 2002). Model (1) includes

two sets of (un-interacted) fixed effects. The tour fixed effect γtour controls for artist fixed

effects because the vast majority of tours in our sample include a single artist. The venue

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fixed effect γvenue controls for venue heterogeneity (physical constraints, location…). In

addition, the model includes four sets of interacted fixed effects. These additional fixed

effects respectively control, among others things, for the possibility that different artists

face different fan populations in different cities, that their average fan varies from year to

year, that the fan population in a given city varies over time, and that some promoters

affect differently different artists. In the next section, we report the results of a number of

simplifications of model (1), which explore different possible sources of unobserved

heterogeneity. To determine which set of fixed effects should be included, we follow Nevo

and Wolfram (2002) and compare fixed and random-effects specifications and compute

Hausman tests of the null that estimated coefficients are unchanged. The interpretation of

the estimated parameters as causal rests on the assumption that the fixed effects and control

variables capture the impact of demand characteristics and omitted variables which may

affect the decision to price discriminate.7

Interaction Variables

Secondly, we test the theory using interaction variables. If the unobserved demand and

supply characteristics affecting revenues through the error term are uncorrelated with our

control variables, then the impact of price discrimination for a concert with characteristics

Yi is [γ2 +Yiγ3]. The vector γ3 measures the impact of variables in Yi on the return to price

discrimination. Gamma 2 is a price discrimination fixed effect, capturing the variables that

systematically differ between concerts that do and do not price discriminate. We can test

7 Note that unobserved demand heterogeneity plays here a similar role as under the standard demand estimation. In a standard demand equation, unobserved demand heterogeneity matters only if the price is correlated with demand shocks. In our model, variations in demand may shift revenue but have no impact on pricing policies. Only variations in demand that are correlated with the price discrimination decision matter. This will happen if the seller conditions pricing policies on information that the econometrician does not observe.

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theoretical predictions on how the variables Yi should influence the return to price

discrimination. Doing so, we use only variability in revenues across concerts that do use

price discrimination; controlling for the unobserved heterogeneity, due for example to self-

selection or omitted variables, among those concerts that use price discrimination. This

approach have proved fruitful in other fields (e.g. Rajan and Zingales, 1998). Its validity

rest on the economic foundations of the mechanisms that justifies that the return to price

discrimination should depend on Yi. To do so, the next section builds upon price

discrimination theory and in particular the theory of ticket pricing (Rosen and Rosenfield,

1997).

4-Results

The results are presented as follows. Table 1 restricts γ3=0 and does not include any

interacted fixed effects. The objective is to estimate the average return to price

discrimination in the concert industry. Table 2 reports the results including interacted

fixed effects for artist-year, artist-city, city-year and artist-promoter. This demonstrates the

stability of our initial results after controlling for a wide variety of unobserved

heterogeneity. Table 3 reports the estimates of interaction effects γ3 and discusses the

determinants of the return to price discrimination.

4-1 Impact of Price Discrimination on Revenue

The first row of Table 1 reports the average increase in revenues associated with the use

of more than one pricing category. Each column corresponds to a different specification:

column 1 reports the results with artist, city and year fixed effects. In column 2, we replace

the artist fixed effects with tour fixed effects. This is a richer specification, since, on

average, each artist is observed for more than 6 tours in the sample (and very few tours

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have multiple artists). The tour fixed effects capture some important features of the show,

because concerts within a tour are usually jointly marketed and often feature a common set

of songs and stage. In column 2, we also replace city fixed effects with venue fixed effects.

Venue fixed effects not only control for the city-specific demographics but also for venue-

specific characteristics, such as location, type (theatre or stadium) and overall experience.

In column 3, we further add a series of promoter fixed effects, capturing the time invariant

characteristics of the main promoter for each concert (note that promoters may vary within

a tour). This is important because the promoter can influence the pricing and marketing of

a concert. Finally, we include in Xi the number of available tickets (overall capacity), since

in some venues the capacity may slightly change depending on the show.

The impact of price discrimination is positive and significantly different from zero at

conventional levels. The magnitude is also economically significant: revenues are 5

percent higher when more than one price is used, and for the average concert in 2005, this

amounts to over $37,000. In 2005 alone, price discrimination accounts for over $50 million

at the industry level.

We compare the results of the fixed effects estimator in column 3 with the

corresponding random effects estimator. We reject the null of no change in the parameters

of the remaining control variables. This suggests that the fixed effects are capturing

relevant unobserved heterogeneity. We further explore the stability of the results in the

next section.

4-2 Controlling for Interacted Fixed Effects

Although in principle one could simultaneously introduce all the interaction terms in

model (1), in practice the flexibility of the specification comes at the cost of a reduction in

degrees of freedom, so we report the results using different combinations of fixed effects.

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In table 2, column 1, we introduce the interaction between artist and city. This captures the

differences in preferences for bands across cities. We also introduce the interaction of artist

and year fixed effects. This is to capture the evolution over time of the production of each

band. In column 2, we include the interaction of city and year fixed effects, to account for

the change in the number and preferences of fans within a city, as well as other time-

varying city-specific characteristics.

In column 3, we include, in addition to artist-year fixed effects, the interaction of artist

and venue dummies: for each band, we allow heterogeneous consumers not only across

cities, but also across venues within a city. In column 4, we further add city specific trends

to capture the within-city change in preferences and demographic variables.8 Finally, in

column 5, we interact the artist and promoter indicator variables, to capture the fact that

pricing strategies are often jointly set by artists and promoters.

In spite of the wide variety of heterogeneity that is accounted for, the impact of price

discrimination is systematically positive and statistically significant at conventional levels.

Price discrimination is systematically associated with an increase in revenues between 4

and 6 percent.

Additional control variables

In Table 3, we enrich the specification in Table 1, column 3 (that includes tour, venue

and year fixed effects) by adding in Xi the number of secondary bands playing in each

concert and the total number of promoters (in addition to the main one) involved in each

concert. These variables provide a more complete description of each concert and its

marketing. We also add month specific fixed effects to account for seasonality in demand.

8 Adding city-year fixed effects reduces too much the degrees of freedom.

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Finally, we add the number of concerts held in the same city and year. This is a measure of

the competitiveness of the market in which a concert is held.

4-3 The Determinants of Price Discrimination

This section investigates the influence of market characteristics on the return to price

discrimination. We estimate the vector of parameter γ3, measuring the impact of

competition, product and demand characteristics, seasonality, and characteristics of the

distribution channel on the return to price discrimination.

According to monopoly theory, the return to price discrimination depends on the

distribution of consumer preferences and the marginal cost function.9 In our application,

however, marginal costs are zero, and even more importantly, physical constraints dictate

the quantity of seats that can be allocated to each category for a given average level of

category quality. The model of Rosen and Rosenfield (1997) matches our application.

They show that the return to price discrimination depends on the joint distribution of

preferences for different seat classes. One would expect the return to be higher when the

degree of differentiation between seat classes is higher (lawn ticket versus assigned

seating) and when consumers have more heterogeneous preferences for seat qualities

(affluent baby boomers versus college student). We investigate these predictions by

considering variables that are likely to influence either the degree of seat differentiation or

the extent of preference heterogeneity. Although the connection with these concepts is

straightforward for some of the variables we use, for others the interpretation is subject to

debate. The value of this exercise ultimately derives from the fact that we can measure a

large number of variables for which an argument can be made that they should matter and

9 Anderson and Dana (2007) show that even in a general setup where the monopolist sets both prices and quantities, it is not always optimal to price discriminate.

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we can check the theory by investigating the sign and significance of their impact on

revenue, as well as their interacted impact with price discrimination.

Venue capacity: The direct impact of venue capacity on revenues is positive and

consistent with the results in the previous tables. In addition, however, capacity has a

negative impact on the return to price discrimination. One explanation for this result is that

high quality seats are those that are located within a small distance from the stage also

known as the golden circles. Beyond this distance, all seats are close substitute. Under

that interpretation, variability in venue capacity mainly reflects changes in the number of

low quality seats, and the gains from price discrimination are not proportional to the size of

the venue. This interpretation is consistent with Rosen and Rosenfield (1997) who predicts

that the return to price discrimination should depend on the extent of differentiation

between products.

Male ratio: the fraction of male members of the band has a negative impact on the return

to price discrimination. This is in line with anecdotal evidence suggesting that male bands

have more homogeneous public, which may imply more homogenous preferences over

quality.

Prominence: more prominent bands (measured by the number of words written in the

biography of an artist on billboard.com) can increase their revenues by a larger margin by

price discriminating. This is in line with the idea that the most prominent bands (e.g. U2,

Rolling Stones) have a more diverse audience because they appeal to a broader

demographic range of the population.

Other product characteristics: The origin of the band (US or foreign), the genre

(classified in three groups: Rock, country and other) and the number of distinct artists

featured, have no impact on the return to price discrimination. Interestingly, the number of

artists featured has a positive direct impact on the level of revenues (independently of

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whether price discrimination is used): each additional band implies 3 percent higher

revenues. Increasing the number of artists increases the level of demand but does not

increase the return to price discrimination.

Population Density (population per square mile, by city): in urban areas the return to

price discrimination is higher, relative to rural areas. This is likely to be explained by the

existence of a more diverse public in urban areas.

Racial heterogeneity:10 Racial homogeneity of the population is measured by the Gini

diversity index (one minus the sum of the squared fraction of population classified as

black, white or other). This is a measure of the probability that two individuals randomly

chosen not belonging to the same racial group. The return to price discrimination is higher

in cities with a more racially diverse population. This is consistent with preferences for

quality being correlated with ethnic origin and heterogeneity being higher across races than

within races. The magnitude of the impact is rather small: a one percent increase in the

heterogeneity index implies a 0.04 percent increase in the return to price discrimination,

Income level and heterogeneity: We compute the Gini diversity index using data on the

fraction of population in different income groups, as reported by the 2000 census at the city

(or place) level. The return to price discrimination is higher in cities in which income

heterogeneity is higher, and the magnitude of the estimated effect is large, although the

standard error is large and the coefficient is not significantly different from zero. The level

of average household income (again by city or place) has a positive and statistically

significant impact on the return to price discrimination. An increase in average household

income of $10,000 implies a 4 percent increase in the return to price discrimination.

Age heterogeneity: We also compute the Gini diversity index using census data on the

distribution of the population across age groups. We find that a one percent increase in the

10 ethnic group.

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age heterogeneity index implies a one percent increase in the returns to price

discrimination.

Occupational diversity: a one percent increase in the Gini diversity index for the

different occupational groups used by the census (management, services, sales, farming,

construction, production) implies a 0.2 percent increase in the returns to price

discrimination. .

Seasonality and time trend: There is a significant increase in the overall level of

revenues in November and December, but there is only weak within-year seasonality in the

returns to price discrimination, with November and December having slightly higher

returns. There is no evidence of any yearly trend in the return to price discrimination.

4-4 Competition

The relationship between price discrimination and competition has received much

attention in the theory literature and has been the focus of some empirical studies (Busse

and Rysman, 2005). Our contribution is to estimate how the return to price discrimination

depends on competition. Stole (2007) reviews the theory literature and concludes that the

welfare effects of increasing competition are typically ambiguous and depend ‘on the

nature of competition, consumer preferences, and consumer heterogeneity.’ Another

consideration that is specific to our case study is information spillover. As we argued

earlier, promoters’ local information plays a key role in setting pricing policies and

promoters’ knowledge is likely to increase in more competitive markets where more events

take place.

We measure competition by the number of concerts that take place in a given market in

a given year. Our measure of competition could be correlated with unobserved city

characteristics but we can control for such heterogeneity by including city fixed effects.

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Concert revenues are on average lower in cities in which a larger number of concerts take

place during the same year, independently of whether price discrimination is used or not.

On average, 10 more concerts imply a 2 percent decline in revenues. This is consistent

with local competition reducing profit margins. The same variable has opposite effects on

the return to price discrimination. On average, 10 more concerts imply a 1.5 percent

increase in the return to price discrimination. These results are consistent with models of

price discrimination and competition (e.g. see Stole (2007) for a review). An alternative

candidate interpretation, which we will revisit soon, is that there is more information

available on how to optimally segment a venue in more competitive markets.

4-5 Promoter and Expertise

There is no direct effect of the number of promoters on concert revenues. However, the

number of promoters is associated with higher returns to price discrimination. One

additional promoter is estimated to increase revenues by 2 percent when price

discrimination is used. This result supports the hypothesis that promoter are instrumental

in the implementation and success of price discrimination. Additional promoters may add

experience and information about market demand that increase the return to price

discrimination.

4-6 Return to Additional Seating Categories

The number of seating categories is relatively low in the concert tour industry.

Although Leslie (2007) reports the same observation in his case study to a Broadway show

(his firm never uses more than two seating categories for a given show), the number of

seating categories can be large for classical music events (Huntington, 1993). Under the

assumption that the seller chooses the number of seating categories, one would expect to

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observe few seating categories if the return from adding categories is low. In fact, this is

the approach taken by Wilson (1996) and Miravete (2007) in the context of non-linear

tariffs. They argue that the menus of tariff options offered in practice are simple because

adding complexity beyond two or three tariff options has a small impact on revenue;

possibly smaller than the associated marketing costs.

While Miravete’s evidence applies to non-linear tariffs, there is to our knowledge no

corresponding empirical study for a product line monopolist which is the other pillar

application of second degree price discrimination. This is despite the fact that many sellers

forgo offering multiple product qualities (Anderson and Dana, 2007). Translated in the

context of our case study, we would expect to find that the return to additional seating

categories should be decreasing and small once a couple of categories are already offered.

In Table 4, we report the average increase in revenues associated with using multiple

seating categories. We include three indicator variables, equal to one when the number of

seating categories is equal to two, three and four respectively (recall that only 4 percent of

the concerts in our sample offer 4 or more seating categories). Table 4, Column 1 and 2

include the same control variables as in Table 1, column 1 and 2. Table 4, column 3

includes artist-year and artist-city fixed effects as in table 2, column 1.

The marginal impact of one additional category is positive but decreasing as the number

of existing categories increases. In column 1, the average increase in revenue associated

with the introduction of the second seating category is 4.6 percent. With the introduction

of a third category revenue further increases by 2.9 percent and with the fourth by only 2.3.

Similar results hold for the alternative specifications, although the decline in the marginal

increase in revenues is stronger for the fourth category.11

11 An interpretation is that price discrimination in the concert tour industry is to a large extent driven by two main consumer segments---affluent baby boomer versus young audience---which is consistent with reports from industry professionals. This

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5-Conclusions

This work is to our knowledge the first systematic non-structural study of the

relationship between price discrimination and revenue at the level of an entire industry.

We follow others who have used a panel data approach to study price discrimination

(Shepard (1991), Nevo and Wolfram (2002), Busse and Rysman (2005)). The richness of

our dataset allows us to test for many possible sources of unobserved heterogeneity; an

option that was not possible to the same extent in some of these studies. Another unique

feature of our case study is that the absence of variable cost permits to unambiguously

associate multiple product offerings (seating categories) with price discrimination.

We find that price discrimination increases revenue on average by 5 percent in our

sample. Interestingly, our baseline result is broadly consistent with Leslie’s (2004)

analysis of a Broadway show, and this is despite the fact that we use a fundamentally

different empirical approach (panel data versus structural micro econometrics), a different

data set (single Broadway show versus large fraction of the popular concert industry),

focus exclusively on multiple seating categories (he considers the joint impact of coupons

use and multiple categories), and that these two industries share few features beyond the

fact that they both produce entertainment events.

In addition, we find that the return to price discrimination increases with competition

and in markets where demand is more heterogeneous, measured either by population

density, or racial diversity, and for bands that appeal to more fragmented public. Finally,

we find decreasing return to additional seating categories. This may explain why only a

dichotomy was the primary reason for price discrimination reported in our phone interviews. Along the same lines, Waddell et al. (2007, p.51) report “older, less price-sensitive audiences…paying more for tickets appreciate having their own piece of ‘real-estate’ in a seat, but often they are adverse to a lawn or general admission

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very small fraction of concerts in our sample use more than three seating categories. An

interesting issue is why the number of seating categories is much higher for classical

events than for concerts for popular music or for Broadway shows.

situation. These fans like to know exactly where they will be and do not find sitting or standing in the grass appealing.”

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References

Busse, M. R. and M. Rysman (2005): “Competition and Price Discrimination in

Yellow Pages Advertising.” RAND Journal of Economics, 36, 378–390.

Connolly, Marie and Alan B. Kruger (2006), “Rockonomics: The Economics of

Popular Music.” Handbook of the Economics of Art and Culture,.

Courty, Pascal (2003b), “Some Economics of Ticket Resale,” Journal of Economic

Perspectives, 17–2, 85–97.

Clerides, Sofronis. (2004) “Price Discrimination with Differentiated Products:

Definition and Identification.” Economic Inquiry 42(3), 402-412.

Huntington,Paul (1993). “Ticket Pricing Policy and Box Office Revenue.”

Journal of Cultural Economics 17(1) : 71-87.

Leslie, P. “Price Discrimination in Broadway Theatre.” RAND Journal of Economics,

Vol. 35 (2004), pp. 520-541.

McManus, Brian. (2008). “Nonlinear Pricing in an Oligopoly Market: The Case of

Specialty Coffee,” RAND Journal of Economics.

Miravete, Eugenio and Lars-Hendrik Röller. (2004). “Estimating Markups under

Nonlinear Pricing Competition.” Journal of the European Economic Association, 2, 526-

535.

Miravete, Eugenio. (2007). “The Limited Gains from Complex Tariffs.” CEPR

Discussion Paper No. 4235.

Nevo, A. and Wolfram, C. (2002). “Why do Manufacturers Issue Coupons? An

Empirical Analysis of Breakfast Cereals,” Rand Journal of Economics, 33, 319-339.

Passman, Donald. All You Need to Know About the Music Business. Penguin Books.

2001.

Reynolds, Andy. ‘The tour book.’ 2007

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Rosen, Sherwin and Andrew Rosenfield. 1997. “Ticket Pricing.” Journal of Law and

Economics. 40:2, pp. 351-76.

Shepard, Andrea. “Price Discrimination and Product Configuration,” Journal of

Political Economy, 99 (1991): 30-53.

Thall, Peter. What They’ll Never Tell You About the Music Business. Watson-Guptill

Publications. New York. 2002.

Verboven, Frank. (1999). “Product Line Rivalry and Market Segmentation with an

Application to Automobile Optional Engine Pricing.” The Journal of Industrial

Economics, 47, 399-425.

Waddell, Ray, Rich Barnet, and Jake Berry. ‘This business of concert promotion and

touring.’ Billboard Books, 2007.

Wilson, R. Nonlinear Pricing. New York: Oxford University Press, 1996.

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Table 1. The impact of price discrimination on concert revenue.

(1) (2) (3) ln(revenue) ln(revenue) ln(revenue) Price discrimination 0.054*** 0.049*** 0.053*** (0.008) (0.007) (0.007) ln(capacity) 0.932*** 0.805*** 0.817*** (0.005) (0.007) (0.007) Artist f.e.? Yes City f.e.? Yes Year f.e.? Yes Yes Yes Tour f.e.? Yes Yes Venue f.e.? Yes Yes Promoter f.e.? Yes Note: The dependent variable is the log of gross revenues ($). The number of observations is 19,550. The number of artist1 fixed effects 123; the number of venue fixed effects 1532; the number of tour fixed effects 811; the number of promoter fixed effects is 424.

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Table 2. The impact of price discrimination on concert revenue (with interaction terms).

(1) (2) (3) (4) (5) ln(revenue) ln(revenue) ln(revenue) ln(revenue) ln(revenue) Price discrimination 0.040*** 0.064*** 0.050*** 0.051*** 0.048*** (0.009) (0.008) (0.011) (0.011) (0.008) ln(capacity) 0.734*** 0.940*** 0.698*** 0.693*** 0.906*** (0.007) (0.005) (0.011) (0.011) (0.005) Artist f.e.? Yes Yes Yes Yes Yes City f.e.? Yes Yes Yes Year f.e.? Yes Yes Yes Yes Yes Tour f.e.? Venue f.e.? Yes Yes Promoter f.e.? Yes Artist-year f.e.? Yes Yes Yes Artist-city f.e.? Yes Artist-venue f.e.? Yes Yes City-year f.e.? Yes Artist-promoter f.e.?

Yes

City specific trend? Yes Note: the number of observations is 21,131.

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Table 3. The determinants of the return to price discrimination. ln(revenue) Fixed effects

Tour f.e.? Yes Venue f.e.? Yes Year f.e.? Yes Month f.e.? Yes

Variables in Xi

ln_capacity 0.850*** (0.010) num_artists 0.029*** (0.007) num_promoters -0.002 (0.010) City competition -0.002***

(0.0008) age 0.206212*** (0.037584) Age2 -0.003004***

(0.000391) Variables in Yi Product:

ln_capacity -0.077*** (0.009) male_ratio -0.091*** (0.027) prominence 0.000026* (0.000010) US band -0.016667 (0.019387) genre1 (Rock) 0.069526 (0.054101) genre2 (Country) 0.016407 (0.027838) num_artists 0.003805 (0.007556) age 0.021539*** (0.005722) Age2 -0.000268*** (0.000073)

Competition: City competition 0.001475*

(0.000769) Demand:

pop_density 0.000007*** (0.000002) Ln(Gini race hetherogerneity index)

0.040027***

(0.011171) Ln(Gini income hetherogerneity index

0.567421

(0.516312) avg_hausehold_income 0.000004***

(0.000001) Ln(Gini age hetherogerneity index)

1.011847**

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(0.440095) Ln(Gini occupational hetherogerneity index)

0.211429***

(0.071987) Distribution channel:

num_promoters 0.022184** (0.011048) Seasonality and trend:

year 0.003230* (0.001944) Month f.e.? Yes

Note: the number of observations is 17,380.

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Table 4. The return to the number of pricing categories. (1) (2) (3) ln(revenue) ln(revenue) ln(revenue) Two price categories 0.046*** 0.047*** 0.034*** (0.008) (0.007) (0.009) Three price categories 0.075*** 0.065*** 0.069*** (0.010) (0.009) (0.012) Four price categories 0.098*** 0.079*** 0.070*** (0.014) (0.013) (0.016) ln(capacity) 0.937*** 0.805*** 0.733*** (0.005) (0.007) (0.007) Artist f.e.? Yes Yes City f.e.? Yes Yes Year f.e.? Yes Yes Yes Tour f.e.? Yes Venue f.e.? Yes Artist-year f.e.? Yes Artist-city f.e.? Yes Observations 21131 19550 21131

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