THE DEMAND FOR LIVE POPULAR MUSIC: A CASE STUDY
A THESIS
Presented to
The Faculty of the Department of Economics and Business
The Colorado College
In Partial Fulfillment of the Requirements for the Degree
Bachelor of Arts
By
George Drabing Hicks
May 2010 THE DEMAND FOR LIVE POPULAR MUSIC: A CASE STUDY
George Drabing Hicks
May 2010
Economics
Abstract
Live popular music is an area of growing importance. With the proliferation of piracy and online music sources, revenue streams for musicians are shrinking in the 21st Century and live performance is becoming one of the last areas for artists to make a living. This study investigates the determining factors in the success or failure of live popular music events by measuring ticket sales. Using a case study of a venue in Santa Cruz, CA, variables describing event context and expected quality of performance are regressed against final box office sales to uncover any existent relationships. Artist exposure, day of the week, and age restrictions are all found to significantly impact final ticket demand for live music events.
KEYWORDS: (Popular Music, Ticket Demand, MySpace) ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS
ature
TABLE OF CONTENTS
I. INTRODUCTION 1
Making A Living As A Musician 2
A Case Study 4
II. LITERATURE REVIEW 6
Performing Arts 6 Sports Attendance 8
Popularity And Perception Of Quality 11
Conclusion 14
III. METHODOLOGY 16 The Model 16 Predicted Results 19
IV. DATA 22 Event Information 22
Artist Information 24 Omissions 26
Dependent Variable 26
Independent Variables - Event 28
Independent Variables - Artist 32 V. RESULTS 38 Multicollinearity 38 Other Regression Tests 40 Estimations OfDemand 41
Significant Variables - Atrium 44
Significant Variables - Main 45
VI. CONCLUSION 47 Limitations 47 Interpretation 49 Future Research And Final Considerations 54
APPENDIX A 57
APPENDIX B 58
APPENDIX C 60
WORKS CONSULTED 61 LIST OF TABLES
4.1 Range Of Popularity Index For Atrium and Main Stages 34
5.1 Presale Regression Results 42
5.2 Doorsale Regression Results 43 LIST OF FIGURES
4.1 Total Number Of Shows In Each Month For 2009 31
4.2 Total Number of Shows On Each Day In 2009 32 CHAPTER I
INTRODUCTION
"One great rock show can change the world. "
-Jack Black, School Of Rock
Music has increasingly become the focus of a range of economic studies. The rapidly changing dynamic caused by technological advances, piracy, and an overall higher saturation of music in the world has caught the attention of top economists searching for explanations. This study hopes to contribute to existing literature through its examination of factors contributing to the success or failure of popular music concerts.
The popular music industry has come a long way since its inception in the early
1950s. Originally a highly concentrated industry due to large barriersto entry, magnetic tape and new recording technologies in the 60sallowed for a great influx of new firms and music. Ever since, the introduction of new technology (CDs, the Internet, MP3s) has contributed to further growth in market saturation. Today,one of the only differences between professionals and amateurs is the financial backing of large media corporations.
Even still, these large corporations are having to scramble to stay on top.
The greatest challenge for musicians of the 21st Century is to find financial
stability. The recorded music industry has lost large amounts of potential revenue to the
pervasive file sharing so common with younger generations. A 2008 report published by
the IFPI (International Federation of the Phonographic Industry) states, "in 2007, global
1 music sales dropped 8% to $19.4 billion, their lowest level in the last 10 years."1
Research by Maltby Limited also estimates that unpaid file sharing has increased from
3.5 billion to 10.8 billion and CD 'ripping' (copying) has increased from 1.3 billion to just over 5 billion between the years 2004 and 2007 2 Although digital sales through companies such as iTunes and Amazon.com have helped to offset these losses, digital sales do not come close to balancing the scales.
Making A Living As A Musician
Traditionally, musicians are discovered by a record label, which agrees to help fund musical endeavors. Contract negotiations ensue, outlining theobligations artists have to their record label in return for an initial investment.Frequently, this begins with a recording contract. The label gives an advance to an artist in order to pay for the services of a recording studio, producers, engineers, manufacturing, marketing, etc. After completion of the creative process, the recording goes on sale. As sales accumulate, the revenue returned on the recording is distributed on a percentage basis, i.e. royalty payments. In the traditional contract, a large percentage of the profits are returned to the record label because of their existing investments in the artist. A portion is also sent to the individuals (outside of the artist or band) associated with the production of the album.
Finally, a small percentage is reserved for the artist(s); however, the record label
withholds a majority of this revenuebecause the artist is in debt for the recording
advance, as well as for other promotional expenses.
1 Maltby Capital Ltd, March 31, 2008 Annual Review, http://www.emigroup.com/NR/ rdonlyres/0753D5E3-20C6-433E-A616DlBC4482BB42/1658/AnnualReview2008.jpg, accessed
October 2009, 18.
2 Ibid. The next step for artists under contract is to tour as an accompaniment to an album release. Again, the label advances all touring expenses and takes a portion of an artist's revenue to help pay it back; however, unlike the recording contract, an artist earns a substantial portion of the profits. This is a result of the greater revenue stream through ticket sales, as well as the complementary revenue received through merchandise sales. In their influential article "Rockonomics", Connolly and Krueger find recordings create profit for record labels and tours provide income for artists. They state, "for the top 35 artists as a whole [of the ones included in their study], incomefrom touring exceeded income from record sales by a ratio of 7.5 to 1 in 2002."3 Any additional royalties collected by artists through publishing contracts are subsequently even less than recording income.4 Given the current climate of the music industry, there is no reason to believe this has changed since 2002. With record sales at an all time low, live music is one of the few ways for musicians to maintain successful careers. Termed the 'Bowie
Theory' by Connolly and Krueger, they quote the famous singer David Bowie, who once said, "Music itself is going to become like running water or electricity. You'd better be prepared for doing a lot of touring because that'sreally the only unique situation that's going to be left."5
Given the importance of touring and live music in the current industry, this study explores indicative elements of live popular music that create a successful tour. Here,
3 Marie Connolly and Alan Krueger, "Rockonomics: The Economics of Popular Music," NBER Working Paper (April 2005): 4.
4 Ibid.
5 Jon Pareles, "David Bowie, 21st-Century Entrepreneur", The New York Times,9 June 2002, quoted in Marie Connolly and Alan Krueger, "Rockonomics: The Economics of Popular Music," NBER Working Paper (April 2005): 24. instead of pooling national touring data, as in the study conducted by Connolly and
Krueger, a case study of a single venue in Santa Cruz, CA, known as The Catalyst,will hopefully break ground for further research and modeling. By using the same venue and
the same population,6 this study diverges from others in the hopes of finding relationships
between artists and ticket sales as opposed to audience members and ticket sales.
A Case Study
The venue under investigation in this study is The Catalyst Nightclub. The
Catalyst provides the ideal case study for the examination of popular music demand for a
number of reasons. Not only is it established in the live touring circuit, it also has two
stages, which allows for greater application to other live music venues. Located on the
downtown strip of Santa Cruz, CA, The Catalyst is conveniently located near restaurants,
storefronts, bars, and several smaller music venues. On evenings of big shows, it is the
epicenter of nightlife in Santa Cruz with lines out the door, various loitering, and
overflow filling nearby bars. The Catalyst has been a music venue since 1969 and its
stage has been graced with the presence of many influential acts: Neil Young, Nirvana,
The Red Hot Chili Peppers, and Willie Nelson, just to name a few. As mentioned above,
The Catalyst is actually comprised of two venues: the atrium stage and the main stage.
The atrium stage is smaller and more intimate. It canhold between 300 and 350 people
and is used for acts wishing to play to a smaller crowd or acts that do not attract a bigger
6 The author acknowledges the fact that different people come from different places to see different musicians at The Catalyst. Here, the same population is considered in a regional sense with the assumption that audience members are not traveling long distances to see a show in Santa Cruz. This is especially true given a majority of these acts also perform in the San Francisco Bay area and surrounding regions. Also, the closest airport is in San Jose, an hour drive, so it is presumed there are few, if any, audience members traveling from outside the state. crowd. The main stage, in contrast, can fit up to 1000 people (albeit tightly) and is the stage traditionally considered when one talks about 'The Cat'.
The main sources of income for The Catalyst, as well asfor most venues of
similar affect, are ticket sales and alcohol sales. Common to most evening social
environments, there is a large bar area at The Catalyst, which, at one point in time, was
the longest in California. Itis estimated, by an employee from The Catalyst, that the
profits gained from alcohol sales are equal to or sometimes larger than the profits
received from ticket sales; however, thenecessity of ticket sales to the profitability of the
club is paramount since alcohol is only purchased after entrance to a concert is paid for.
This study hopes to contribute compelling information in order to assist in the business
decisions of music venues like The Catalyst, so club owners can maximize their
profitability. It also assists musicians, who are attempting to make a living performing
music, by giving some perspective on the types of music events that succeed or fail.
The following chapter provides an overview of existent literature as it relates to
this paper's study. Chapter three outlines the method ofapproach taken for conducting
this research and chapter four offers a synopsis of the data collected. Finally, chapter five
summarizes the results of each regression within this study while chapter six presents an
application of the data as it relates to the business operations of The Catalyst. CHAPTER II
LITERATURE REVIEW
Current literature regarding attendance at popular music events is limited, if existent at all. For this reason,the review compiled here is broken into sections, focusing on specific dimensionsof the study at hand. A brief overview of studies pertaining to the performing arts is covered first to give perspective on the change in approach of this paper. Then, a summary of current literature relating to the estimation of sporting event attendance, upon which the equations in this study base themselves, is presented. Finally, a review of the literature associated with artist ranking and online activity grounds the reasoning behind the inclusion of associated variables and their potential impact.
Performing Arts
Beginning in 1966, with the seminal work of Baumol and Bowen, Performing
Arts - The Economic Dilemma, the arts have steadily gained attention in the field of economics. The primary focus of their book, as well as other studies in this field, is what will be referred to here as the fine arts: theatre, opera, classical music, etc. In this, usually non-profit, segment of the arts, studies attempt to "know what properties of a population member make it more likely that he or she would attend."x The common objective is
1 Francesca Borgonovi, "Performing Arts Attendance: An Economic Approach," Applied Economics 36 (2004): 1873. policy-oriented: finding ways to maintain and/or increase attendance to these 'dying' arts.
Unfortunately, these studies are only distantly related to the one conducted here since popular music is exactly that, popular. Still, a brief overview of the more pertinent studies
in the field of cultural economics follows.
Multiple studies examine learning by consumption. In an examination of theatre
demand, Levy-Garboua and Montmarquette find the more individuals attend theatre, the
more likely they are to attend again.2 In a separate study by the same individuals, they
continue to say, "even though the average individual might have initially more taste for
classical music than for popular music, she would end up liking popular music better
after a while because she was not exposed to classical music."3 Kurabayashi and Ito
empirically confirm this same conclusion in their 1992 study.4 This helps to explain the
proliferation of popular music over classical music, since media in society is infiltrated
with 'Top 40' music, not classical music. This also relates itself to an explanation of the
'superstar phenomenon,' which is covered at the end of this chapter.
Similar to the conceptof learning through consumption, studies examine what
role arts education plays in the demand for arts. A study by Francesca Borgonovi finds
"that participation in art education is a much more important determining factor of
attendance than any other personal characteristic, including general educational
2 Louis Levy-Garboua and Claude Montmarquette, "A Microeconometric Study of Theatre Demand," Journal of Cultural Economics 20 (1996): 25.
3 Louis Levy-Garboua and Claude Montmarquette, "The Demand for the Arts," Cirano Working Papers Scientific Series (2002): 4.
4 Yoshimasa Kurabayashi and Takatoshi Ito, "Socio-economic Characteristics of Audiences for Western Classical Music in Japan: A Statistical Analysis," in Cultural Economics, ed. Ruth Towse and Abdul Khakee (Berlin: Springer-Verlag, 1992), 275-87. attainment."5 In a similar study including both classical and popular music listeners,
Prieto-Rodriguez and Fernandez-Bianco find a positive relationship between educational status and consumption.6 The same study also concludes that age has a negative impact
on popular music demand.7 Regrettably, these studies do not relate closely to thecurrent
study of The Catalyst, but by considering the last mentioned above; it can be concluded
that there is a considerable market for popular music in Santa Cruz given its large
population of students at The University.
Sports Attendance
Since analysis of attendance for popular music events is limited, this paper
models itself after the exhaustive body of literature regarding attendance at sporting
events. Sporting events relate to live, popular music events on multiple levels: they are
social activities, typically profitable due to high levels of popularity, and are experience
goods, whose quality and value is known only after consumption. For these reasons,
sporting event studies are the most logical point of imitation for a study of live music
demand. The following provides a concise overview of the research taken into
consideration during the development of this paper's estimation method.
A review of sports economics studies by Schofield suggests, when dealing with
attendance studies, the theory of consumer demand provides "factors [that] would be
5Borgonovi, 1884.
6 Juan Prieto-Rodriguez and Victor Fernandez-Bianco, "Are Popular and Classical Music Listeners the Same People?" Journal of Cultural Economics 24 (2000): 160.
7 Ibid. expected a priori to beof some influence on attendance."8 He continues to say these factors can be grouped into four categories: economic variables, demographic variables,
game or team variables, and residual preference variables (such as weather or day of the week). This notion is applied to the study of all different sports (cricket, football,
baseball, hockey) and assists researchers in assuring the inclusion of all potential factors.
Almost every one of the following papers, as well as this paper, usesa classification
system similar to the above in order to develop a comprehensive and predictive model of
attendance.
Another common trend of attendance studies is the use of tobit regression
analysis. Tobit estimation allows for the censoring of observations beyond a defined
value. It takes into account the potential surplus of demand that exists when a stadium
reaches capacity. In some cases, the censoring value is set to the capacity of the stadium
under question, but in others, percentages of tickets sold are formulated and the censoring
occurs at 100%. The latter approach is used in this study, and is explained in the
subsequent chapter.
A study of cricket match attendance by Paton and Cooke usesa combination of
the above to find that time and location has a significant impact on attendance. In their
particular model, they censor observations at stadium capacity, use total attendance
values, and create a series of numerical and indicator variables accounting for changes in
event context. Another study, taking a similar approach as Paton and Cooke, investigates
8 John Schofield, "Performanceand Attendance At Professional Team Sports," Journal of Sport Behavior 6, No. 4 (Dec 1983): 200.
9 David Paton and Andrew Cooke, "Attendance at County Cricket: An Economic Analysis," Journal of Sports Economics 6, No. 1 (Feb 2005): 40. 10
NHL attendance and usesa dependent variable defined as attendance per home game.
Here, as well, tobit censoring occurs where attendance equals arena capacity.
Other studies regarding attendance modify the dependent variableso it is expressed as a percentage of capacity. Welki and Zlatoper's study of professional football in 1991 uses a censoring value equal to attendance capacity.11 Their larger study of 1986 and 1987, however, manipulates the dependent variable so it is a percentage. Welki and
Zlatoper defend their position by stating percentages, instead of whole values, allow for
the adjustment of stadium sizes.12 The same thought process is applied to the current
study; percentages allow for the demand of two stages with different capacities to be
compared. Both studies also quote Schofield when defending their inclusion of a variety
ofindependent variables, stating they strategically fit into the four categories defined by
Schofield.
A study of MLB spring training attendance groups the independent variables of its
study "into one of three categories: measure of location and stadium characteristics;
measures of expected game quality; and time and weather factors."13 A separate paper
regarding college football attendance also takes this approach.14 This closely resembles
10 John Leadley and Zenon Zygmont, "When Is the Honeymoon Over? National Hockey League Attendance, 1970-2003," Canadian Public Policy 32, No. 2 (Jun 2006): 218.
11 Andrew Welki and Thomas Zlatoper, "US Professional Football: The Demand for Game-Day Attendance in 1991," Managerial and Decision Economics 15 (1994): 492.
12 Andrew Welki and Thomas Zlatoper, "U.S. Professional Football Game-Day Attendance," Atlantic Economic Journal 27, No. 3 (Sept 1999): 287.
13 Michael Donihue, David Findlay, and Peter Newberry, "An Analysis of Attendance at Major League Baseball Spring Training Games," Journal of Sports Economics 8, No. 1 (Feb 2007): 43. 14 Donald Price and Kabir Sen, "The Demand for Game Day Attendance in College Football: An Analysis of the 1997 Division 1-A Season," Managerial and Decision Economics 24 (2003): 36. 11 the method of this paper's estimation equation; however, since location and venue stay constant throughout the dataset, the classifications are reduced to two groups. The first
group defines the context of each event and the second relates to the expected quality of
each performance. The next section consists ofan overview of the different ways
expected quality is measured.
Popularity and Perception of Quality
Any study regarding the arts must take into account the rather subjective and
unquantifiable aesthetic experience it provides individuals. When it comes to music, a
number of studies attempt to estimate this 'quality' or 'stardom' factor as it contributes to
the overall success or failure of a musician. Sherwin Rosen originally coins the term
'Superstar Phenomenon' in his 1981 article, "The Economics of Superstars."15 It
describes the situation where relatively few people earna significant portion of the total
returns in an industry. This is certainly the characteristic of the popular music industry,
but is also the dynamic of many other industries including textbooks, sports, and movies.
There is some debate, however, as to what aspect of these 'superstars' in the music
industry is the determining factor contributing to their success.
A study conducted by Hamlen investigates the predictors of record sales for
singers and finds, first and foremost, that career longevity playsa significant role in
predicting record sales. More interestingly, though, is the study's use of harmonic content
measurements to assess the quality of a singer's voice. The variable measuring voice
quality is found to be significant, meaning consumers of popular music discern quality in
15 Sherwin Rosen, "The Economics of Superstars," The American Economic Review 71, No. 5 (Dec 1981): 845. 12 singers (according to this particular measurement); nonetheless, the coefficient is
"significantly less than one," showing quality of voice is ultimately not largely related to overall success.16 Hamlen uses this to disprove the concept of the superstar phenomenon, but, as other studies suggest, the phenomenon may not be a result of any variations in
actual talent.
A separate study regarding thesuperstar phenomenon by Chung and Cox
concludes similarly, stating disparity between artists can exist "among individuals with
equal talent."17 They see superstardom as a result of probability mechanisms rather than a
result of talent differences. This supports the notion that a measure of quality may not be
necessary for the purpose of this study.
Another study investigating superstardom in popular music by Moshe Adler
suggests stardom develops where consumption requires knowledge. He states, "the
acquisition of knowledge by a consumer involves discussion with other consumers, and a
discussion is easier if all participants share common prior knowledge." Since music is
an experience good, Adler argues the social activity associated with music provides a
greater contribution to the success or failure of a musician than actual talent does. This
further defends the position, for the study of popular music, that a measure of quality may
not be necessary. Instead, a measure of social activity surrounding a musician, especially
online, might be more predictive.
16 William Hamlen, Jr., "Superstardom In Popular Music: Empirical Evidence," The Review of Economics and Statistics 13, No. 4 (Nov 1991): 731-2.
17' Kee Chung and Raymond Cox, "A Stochastic Model of Superstardom: An Application of The Yule Distribution," The Review of Economics and Statistics 76, No. 4 (Nov 1994): 775.
18 Moshe Adler, "Stardom and Talent," The American Economic Review 75, No. 1 (March 1985): 212. 13
In an investigation of user comments on the social networking website MySpace, and their relation to a surveyof young adults, Julia Grace et al. finds online activity provides a better indicator for popularity than traditional record charts like Billboard.
They conclude, "new opportunities for self expression on the web provide a more accurate place to gather data onwhat people are really interested in than traditional methods."19 This suggests a measurement of online, social activity may prove to be more beneficial than a subjective measure of quality.
Koenigstein and Shavitt investigate the queries of peer-to-peer (P2P) file-sharing systems as they relate to the success of various songs. Similar to the above study, they find a strong correlation (0.89) between the song rankings of Billboard and their own rankings based off of P2P networks. They evengo so far asto say the list based off of
P2P networks "has a weak advantage over the Billboard list."
Yet one more study, by Dhar and Chang, looks at the volume of activity on blog posts,in addition to changes in MySpace friend counts, to investigate how each impacts the success of music sales. They find "the entire dataset strongly suggests] that higher blog post volumes and higher percentage changes in MySpace friends correspond to increased weekly sales in the future."21 Dhar and Chang suggest activity on user-
19 Julia Grace, Daniel Gruhl, et al., "Artist Ranking Through analysis of On-line Community Comments," The 17th International World Wide Web Conference (2008): 9.
20 Noam Koenigstein and Yuval Shavitt, "Song Ranking Based On Piracy In Peer-To- Peer Networks," 10th International Society for Music Information Retrieval Conference (2009): 633. 21 Vasant Dhar and Elain A. Chang, "Does Chatter Matter? The Impact of User- Generated Content on Music Sales," CeDER Working Paper New York University (Feb 2008): 15. 14
generated websites, like MySpace and blogs, have strong predictive power in regards to how music is received by the public and the success of music sales.
Francois Abbe-Decarroux's study focusing on the impactof quality on demand
finds, "it is necessarily the consumer's perception of quality prior to consumption that
applies."22 In this case, it becomes an issue of risk aversion when audience members
choose to attend a music event or not. If there are aspects ofan artist that give greater
likelihood for a good performance, attendance increases. Abbe-Decarroux believes
perception and anticipation is the factor making an economic impact on demand. It might
be hypothesized, then, that online activity, as described above, increases attendance
levels, so long as audience members associate the activity with expected quality.
Conclusion
The study of live, popular music is an area of limited research. Traditionally, it
has not undergone the financial challenges to create cause for investigation, but this is
readily changing. Cultural economic studies, up to this point, have aimed to maintain the
existence of fine arts because they are the most susceptible to a lack of funding; however,
in this day and age, where music is perceived as nearly a free, public good, there is a need
for an exploration of the bigger picture, so all types of music endure. This study hopes to
contribute to a contemporary collection of research focusing on the demand for, and
success of, various live music scenarios.
Since there are no studies in this field with anapproach similar to this study,
original formulation of procedures is gleaned from another industry, sporting events.
22 Francois Abbe-Decarroux, "The Perception of Quality and the Demand for Services," Journal of Economic Behavior and Organization23 (1994): 100. 15
Additional factors, however, relating to the perception of quality and thesubjective traits of music consumption are also included so greater perspective on the demand characteristics of a population can be inferred. The following chapter outlines the procedure and equations used for the analysis of ticket demand at The Catalyst in 2009. CHAPTER III
METHODOLOGY
Using a similar approach to the one outlined by Schofield in the previous chapter, this study groupsindependent variables into two classes: one for event context and another for expected quality of music. It builds off of the methods taken by many
researchers in the field of sports economics and estimates a demand curve for attendance
using tobit regression analysis. In addition, the variables relating to expected quality of
the music performed are quantified through use of public, online resources. This chapter,
first, presents the equations to be estimated before discussing the variables involved.
Lastly, it considers the potential impacts each variable has on the final estimation.
The Model
Four separate regressions of the 2009 data must be run for two reasons. First,
there are significant differences between the acts to perform on the main stage versus the
atrium stage. There are also differences in the size of the audience for either space.
Second, information regarding ticket sales before events and for sales at the door is held
separately. If a show sells out before door-sales begin, there is no ticketing information
for that show in the door-sales database. For this reason, independent regressions forpre-
sales and door-sales are executed.
16 17
This study uses tobit regression analysis instead ofan OLS approach because the dependent variable is expressed as a percentage. The dependent variables SoldBefore and
SoldDoor measure the percentage of sales given a set amount of tickets available. Since the two dependent variables do not exist outside the range 0 to 100, a purely linear OLS regression does not suffice. A tobit analysis allows for a defined range of dependent variables and takes into account the fact that no values exist outside the range.
The generalized forms of the model being tested in this study are provided in equations 3.1 and 3.2 below. Indicatorvariables are written in all upper case to differentiate from numerical variables. For a complete definition of each variable, refer to appendix A.
SoldBefore = Po + Pi PriceBefore +p2 Onsale + p3 Popularity + p4 Years + p5 Productivity
+ p6 Rainfall + p7 HOLIDAY + p8 EARLY + p9 ALL + p10 TWENTYONE + p,, JAN +
P,2 FEB + p13 MAR + p14 MAY + pIS JUN + pI6 JUL + p17 AUG + p,» SEP + p19 OCT +
p20 NOV + p2i DEC + p22 MON + p23 TUE + p24 WED + p25 FRI + p26 SAT + P27 SUN +
p28 ROCK + p29 METAL + p3o REGGAE + p3, POP + p32 HIPHOP + p33 FOLK +
p34 PUNK + p35 RNB + p36 FUNK + p37 OTHER (3.1)
SoldDoor = Po + Pi PriceDoor + (32 Onsale + p3 Popularity + p4 Years + p5 Productivity +
P6 Rainfall + p7 HOLIDAY + p8 EARLY + p9 ALL + p[0 TWENTYONE + pn JAN +
p12 FEB + p13 MAR + P14 MAY + p15 JUN + p,6 JUL + p,7 AUG + p18 SEP + pl9 OCT +
p20 NOV + p21 DEC + (322 MON + p23 TUE + p24 WED + p25 FRI + p26 SAT + p27 SUN +
p28 ROCK + p29 METAL + p3o REGGAE + p31 POP + p32 HIPHOP + p33 FOLK +
p34 PUNK + p35 RNB + p36 FUNK + (337 OTHER (3 2) 18
The two equations listed are used to estimate pre-sale and door-sale information for either stage at The Catalyst. The equations vary slightly between atrium and main stage because there are no 'all-ages' events in the main data and no events that fall on a federal holiday in the atrium data. These variables are omitted from their respective equations during analysis, since computer programming drops them if entered into a regression.
There are eight independent variables included in the regression expressing the varying context of each event. They describe any fluctuations in the success or failure of an event not directly related to the artist performing on stage. For this part of the
regression, the data is straightforward and objective in nature. The variables include price
of the event, duration tickets went on sale, whether it rained at all, if the event was held
on a federal or state holiday, what age restrictions were in place, and a series of indicator
variables expressing the date and time of each event. By using ticketing data forjust The
Catalyst, this study assumes constant all other potential variablesfactoring into how a
venue is operated and received by the public. More specifically, this study assumes The
Catalyst extends a constant amount of marketing and publicity towards all the events it
holds.
Four additional variables are included in this study in order to describe the more
qualitative characteristics of the musical acts performed at The Catalyst in 2009. Itis a
weakness of any study involving the arts to try and quantify subjective traits associated
with human perception, but specific objective measures help to illuminate the more
abstract in the case of this study. A quantitative approach has been developed to evaluate 19 each of the following aspects of an artist or group: popularity, career length, productivity, and genre.
Predicted Results
A number of relationships between the independent variables of this regression
and ticket sales are tested and assumed to exist because of discussions between the author
and an employee at The Catalyst. Through his experience working at the club, this
employee has developed an understanding of a number of impacting factors to help
determine whether a show will sell or not. For example, if it rains during the start ofan
event, there is a strong likelihood for door sales to suffer. Similarly, if it is a holiday,
there is an expected, weaker turnout. The type of music also plays an important factor in
regards to what types of people attend and how many there are. An investigation outside
the scope of this study, but believed to exist as well, might test the relationship between
types of music and the location of origination of audience members. Itis believed certain
genres attract more people from specific cities surrounding Santa Cruz than other genres
do. Demographic and regional research relating to audience attendance is a large area for
future study.
Taking into account the assumed impacts of ticket demand via employee
interaction and adding variables to test these impacts and others are the foundation of this
paper. In addition to investigating the relationships between event context and ticket
demand, though, it must be assumed the artists on stage impact ticket demand as well.
The question then becomes: in what way do artists differ and what differences cause 20 changes in demand? This is the cause for a development of generalized statistics describing an artist's popularity, career length, productivity and genre.
If accurately quantified, it would be expected that the more popular artists are, the more likely they will sell tickets. The variable Popularity seeks to confirm this, though it may not be as straightforward as expected since this study usesa specific, regional audience that can have other, potentially offsetting demand considerations. Years, a variable measuring career length, investigates any possible relationships between the age of the music being played and its demand. The Catalyst is considered an old venue, so
musicians that have been playing the same music on the same stage for more than a
decade could be attracting a larger crowd than newer artists. Although the opposite is also
possible since Santa Cruz has a large student population: younger musicianscould be
attracting the greatest audiences.
The variable Productivity centers around yet another continuum on which to
describe musicians. Every artist creates on his or her own time schedule. Some create
prolifically, with vast quantities of music, while othersinvest all their time and effort into
only a few projects. The purpose of this variable is to look into these differences and
judge whether the public responds more strongly to one approach over the other. It might
be idealized that the quality of output instead of the quantity would play a greater role in
determining the success or failure of artists' careers, but it could also be argued that
greater exposure for artists create more musical opportunities for the public to connect to.
The chapter to follow outlines the dataset developed for this study, discussing
each variable in greater detail. The subsequent chapter then compiles the results of the 21 four regressions run before the final chapter draws conclusions and discusses the implications for future study. CHAPTER IV
DATA
Multiple sources were used for the collection of data in this study. Following John
Schofield's approach, as discussed previously, the variables of this study are grouped into categories based on the information they provide the final equation. Since demographic information is not included and factors regarding varying locations are not necessary, the variables of this study are considered in two groups: event context and expected quality of performance.This chapter presents all of the sources consulted for the collection of data as well as an overview of each individual variable as it relates to the final equation.
For a complete table of descriptive statistics for each variable, refer to Appendix B (Table
7.2 for numerical variables and Table 7.3for indicator variables).
Event Information
The primary source of data within this study is derived from the ticketing service used by The Catalyst for all of their event bookings, InTicketing.com. In Ticketing began in 1999 and aims to provide "an ethically conscious, yet economical option to music promoters and fans... a tech-savvy resource for the little guy to work on par with the big
22 23 boys."1 Over the past decade, it has developed into an effective alternative to the larger
online ticketing services of Ticket Master and Live Nation.
The In Ticketing website lists any events The Catalyst enters into the system.
Ticketing staff at The Catalyst has sole access to the account and enters all information
about individual events, so data within the ticketing service is assumed to be accurate.
Any purchases made by consumers prior to the night of the event are held in the ticketing
database. This includes, not only purchases made online, but also all transactions
performed at The Catalyst box office or bar before an event. The online service provided
by In Ticketing closes an average of three hours before the start of any event, so
information on total ticket sales must be divided into two separatedatabases; one for pre-
sales (from the online database) and one for door-sales. The sales information for tickets
purchased three hours before an event and later is compiled by The Catalyst's box office
and saved along with any other transaction records regarding each event
Information gathered off the website includes event details such as artist, date,
time, pre-sale ticketprice, age restrictions, which stage is used, period of availability,
tickets still available and total tickets sold beforea show. The information held by The
Catalyst box office contributes door-sale ticket price, as well as the total number of
tickets sold at the door thenight ofan event.
Data for the variable measuring the impact of weather on ticket demand comes
from The University of California Statewide Integrated Pest Management Program
1 In Ticketing Website, "our history," http://www.inticketing.com/info.php?i=2799, accessed Feb 23, 2010.
2 It shouldbe noted that there exists the possibility for some events to not be included in the online data should a decision by The Catalyst staff prohibit the sale of tickets to the public online for any reason. These potentially 'undocumented' events will not be considered. 24
(UCSIPMP). The UCSIPMP has eight-inch diameter gauges located all over the state of
California measuring daily-accumulated precipitation. The location of the gauge used in this dataset is approximately two and a half miles away from The Catalyst, so for the purpose of this study, it is assumed the two locations experience similar precipitation each day. The exact distance between the two locations is calculated by entering the coordinates of either location into a triangulation formula provided online.
To investigate what impact federal holidays have on event attendance, the website provided by California's Department of Personnel Administration is consulted. It should be noted that the state of California not only abides by all federal holidays, but also observes Cesar Chavez Day. Crosschecking the dates of Catalyst events with the federal holiday schedule provided by the Department of Personnel is the final contribution to the descriptive variables of each event.
Artist Information
A major source of datadescribing the artists performing at The Catalyst in 2009 is derived from the online social networking site, MySpace. Originally intended as a site for individual people to connectwith each other, MySpace has created a common, and
secure, place for musicians to share music, as well as market themselves. It has grown,
since its creation in 2003, to theextent that nearly every act to perform at The Catalyst in
2009 has an associated MySpace page.
MySpace websites are user-generated, so what is displayed on any artist's site is
to be considered a decision of the artist, or any directly associated parties. This means
content provided on these MySpace pages is first-hand information from the musicians. 25
The genre classification of the musicians in this study uses these descriptions given on
MySpace to define and group the various styles of acts performed at The Catalyst in
2009.
One final source provides the information pertaining to the creative output of the musicians involved in this study. The All Music Guide (AMG) and website,
Allmusic.com, seeks to create "the most comprehensive music reference source on the planet."3 With a listed staff of 47 editors and major contributors in March 2010, AMG has created a database containing information about nearly 100,000 artists. Since there is an editorial staff at AMG presumably ensuring the accuracy of their database, it is considered a more reliable source than user-generated sources on the Internet, such as
Wikipedia.com. From this website, the total number of major albums created by individual artists, in addition to the date of their first major album release, is added to the dataset. For lesser-known artists or for artists where information through AMG is inconsistent, personal websites and social networking sites (content created and overseen by artists) are consulted to get the most accurate information possible regarding album releases and original album release dates. One additional website used to gather and double-check this information for a few reggae artists was Unitedreggae.com. United
Reggae is an online reggae magazine with substantial discographies for some of the reggae artists in the 2009 data.4
3 All Music Guide Website, "About Us," http://www.allmusic.com/cg/ amg.dll?p=amg&sql=32:amg/info_pages/a_about.html, accessed March 1, 2010.
4 Use of United Reggae Magazine is a result of reggaemusicians involved in this study linking from their own website to this one for complete discographies. 26
Omissions
Certain observations in the original ticketing information must be excluded for a number of reasons. First, events held at The Catalyst that do not fall under the category of a single, headlining musician being paid for their services are omitted. Most often, these are amateur events such as "Battle Of The Bands" and "Showcases," as well as events withoutmusical performances, including two "Rock Band Contests" (the videogame) and high school parties held by The Catalyst called "VIP Dance Nites" and "@ The Cat".
Also excluded are any events where ticketing is not dealt with through The Catalyst and
In Ticketing, or where tickets are not sold individually. For example, a single person or a
company renting out a stage at The Catalyst deals with the distribution of tickets
independently.
To seek a more precise relationship between ticket sales and individual artists,
nine observations with multiple headliners are omitted. A one-time reunion show in 2009
by a group named "Superbooty" is excluded because they are no longer a professional,
touring act. Also, four acts with incomplete information about either door sale
information or MySpace information are excluded since they do not fit the exact
regression model. One final event, which turned out to be a stand-up comedy act, is also
omitted for obvious reasons.
Dependent Variable
The dependent variable under investigation in this paper is the number of tickets
purchased for a given event held at The Catalyst in the year 2009. As was mentioned
prior, the data is divided into pre-sale and door-sale information, as well main stage and 27 atrium information. Since The Catalyst has two stages and there is a substantial disparity between the sizes of the two, as well as differences in the types of performances to take place on either stage, data is held independently.
The quantity demanded in this investigation is expressed as a percentage of tickets available instead of as a sum total. This is a reflection of the associations between regressions since the number of tickets still available at the door for any show is dependent upon how many tickets are already sold. Because the data is cleanly separated into pre-sales and door-sales, the quantity of tickets available for pre-sale is equal to venue capacity. The quantity of tickets still available for door-sales is then defined as the
capacity less the number of tickets sold during pre-sale. The total tickets sold at each sale
period is then divided by theavailable quantities, giving a percentage showing how well
an artist sells the space they perform in. Refer to equations 4.1 and 4.2 below for the
complete formula ofeach variable.
Tickets Sold Before % Tickets Sold Before Show Capacity (4 \)
Tickets Sold Door % Tickets Sold Door show Capacity _ Tickels Soid Before ^
In the 2009 event data for The Catalyst, there is at least one sold out show for
each equation, where the dependent variable equals one hundred. This occurs only once
in the atrium data (for both pre-sale and door-sale) but happens with 30 shows in the
main stage, pre-sale data. The total number of sold out shows seems to follow the
industry average for the main stage. The previously mentioned paper by Connolly and 28
Krueger states, "about a third of popular music concerts currently sell out."5 This explains why the MainDoor regression has so few observations. Once a show sells out, there are no more tickets available for sale, so those observations are omitted from the subsequent regression. The average percentage of total tickets sold at the door for both stages is around 25%. This, compared to the 65% average for MainPreSale, tells us that if a show does not sell well before the night of an event, there is a small likelihood for door-sales to do better and make a greater contribution to final sales.
Independent Variables - Event
The two variables expressing the price of events in the each regression are
PriceBefore and PriceDoor. There are two different variables used for the two different regressions beingrun because ticket prices change depending on whether they are sold beforea show or at the door. The ticket price used for thevariable PriceBefore is the
quoted price on In Ticketing and does not include convenience fees. These fees are not
used in this study because they are only charged when purchases are made using the
online service. Individuals purchasing tickets at the box office during pre-sales or door-
sales do not pay convenience fees, so for consistency they are omitted. The values used
for PriceDoor are the ones stated in the transactions records of The Catalyst box office.
California state tax is also excluded from both pre-sale and door-sale prices because it is
a fixed five percent of any purchase and is applied to all tickets sold. Including it in the
price of a ticket for this study would not contribute anything significant. Use of the list
price follows the method of Connolly and Krueger, who reason the list price is "relevant
Connolly and Krueger, 25. 29 from the standpoint of artists and promoters, as their ticket revenue is derived from tickets sold at the list price."6
In the pricing information for 2009 of both stages, there is an average increase of three dollars from pre-sale to door-sale price. It is expected The Catalyst raises its prices at the door for events in order to encourage pre-sales. There is also a clear indication in the descriptive statistics that The Catalyst charges on average, $10 more for main stage shows than atrium shows.
Onsale is the numerical variable expressing the number of days tickets are available for sale before an event. The In Ticketing database, for every event, lists the day tickets go on sale, as well as the day tickets go off sale. In 2009, the date and time tickets go off sale through the In Ticketing site is approximately three hours before doors open to the event. If an event completely sells outbefore the day of an event, tickets are listed as going off sale whenever the last ticket was sold. 2009 data, however, does not haveany events with this scenario.
Rainfall is the final numerical variable assigned to the data describing the context of a given event. Measured in inches, it states the total accumulated precipitation on the day of each observation. The collection site for the rainfall data is located approximately two and a half miles from The Catalyst and it is assumed the two locations experience
similar weather patterns. Even though this variable accounts for accumulated rainfall over the course of a full day, it provides some insight into whether a show experiences a
decrease in door sales due to inclement weather on the same day.
The first indicator variable of the regression further describing the context of an
event is HOLIDAY. This variable triggers if an event at The Catalyst occurs on the same
6 Ibid, 13. 30
day as a Federalholiday or, for the case of California, Cesar Chavez Day. It should be noted that only the exact date is considered, there is no account for holiday weekends or similar.
The start time of each show is described by the indicator variables EARLY and
LATE. Exact start times for shows in the data range from seven o' clock to nine thirty, but in order to try and find more general relationships, start times are grouped into two categories. LATE shows are defined as 8:30 PM and later while EARLY shows are 8:00
PM and earlier. Another note, the quoted start time of an event is actually the time when the doors to an event officially open; it is not until approximately one hour later that any music is played.
The Catalyst places age restrictions on a majority of their shows and either limit to ages 16 and older or ages 21 and older. The greatest difference between these age limits during events relates to how alcohol consumption is controlled. For any events with persons under the legal age to drink, The Catalyst ropes off the bars and balconies to create a designated, over 21, drinking area. This greatly differs from a 21+ event, where there are no restricted areas. For this regression, the age restriction for each observation is recorded by an indicator variable that is either SIXTEEN or TWENTYONE. Inthe
2009 atrium data, there arealso four 'All Ages' events, so an additional indicator variable in the atrium stage regressions, listed as ALL, sees iflifting age restrictions has any impact on ticket demand.
The final variables describing the context of events are indicator variables for the day of the week and also the month. This serves to investigate whether specific days or
specific months contribute to the success of The Catalyst's events more than others. It 31 should be taken into account that The Catalyst,in booking their events, consider these
issues, so the data already has a concentration of events at the end of weeks and during
the months when The University is in session. Figure 4.1 and 4.2 below show the
frequency of events for each month and each day to givea sense of the booking decisions
at The Catalyst.
FIGURE 4.1
TOTAL NUMBER OF SHOWS IN EACH MONTH FOR 2009
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Month _ _. .._ I 32
FIGURE 4.2
TOTAL NUMBER OF SHOWS ON EACH DAY IN 2009
MON TUE WED THU FRI SAT SUN Day Of The Week
Independent Variables - Artist
The variable Popularity is an index constructed around the accumulated 'friend count' of artists on the social networking website, MySpace. In a survey ofeveryday users of social networking sites, Danah Boyd finds that, other than actually knowing individuals offline, the greatest causes for creating friendships is to evoke popularity levels, indicate admiration for another (musicians, celebrities, etc.), or to show one's interests.7 Additionally, a separate study conducted by Beuscart Jean-Samuel and
Couronne Thomas finds reputation and popularity of artists on MySpace follows the 80-
7 Danah Boyd. "Friends, Friendsters, and MySpace Top 9: Writing Community Into Being on Social network Sites." First Monday 11, No. 12 (Dec 2006). 33
20 rule commonly associated with cultural goods.8 This further supports the notion that
MySpace activity is an echo of the popularity levels existent within all popular music. For the above reasons, and others reviewed in chapter two, this studyuses friend count on
MySpace as a measure of popularity.
In order to accurately quantify a measure of friend activity on MySpace between varieties of musicians, friend count has to be manipulated. In the investigation mentioned
in chapter two, Dhar and Chang conclude, "it is possible an artist's MySpace friends
count is bloated with individuals who only passed by the artist's profile once, and are not
very interested."9 In their study, Dhar and Chang take the natural log of the percent
change in friend count. For this study, the square root ofan artist's MySpace 'total friend
count' is taken to help control for this potential 'bloating'. Then, to take into
consideration the large range of dates when websites are first created, the friend count is
divided by the number of days since each website was first published. This creates an
index essentially measuring the daily friend activity of any artist to perform at The
Catalyst in 2009, with an adjustment for the exponential growth rate of 'total friend
count'. To adjust for the resulting small numbers, and to increase the consistency of the
regression interpretation, the number is multiplied by one hundred. This brings the entire
range up to 0.778 to 42.65, from 0.00778 to 0.4265. See equation 4.3 below for a
complete formula.
8 Beuscart Jean-Samueland Couronne Thomas, "The Distribution of Online Reputation: Audience and Influence of Musicians on MySpace," Proceedings of the Third International ICWSM Conference (2009): 187-90.
9 Dhar andChang, 2008. 34
Total MySpace Friends*1'2 Popularity = x 100 # of Dayson MySpace
Two things should be noticed when comparing the ranges of the popularity index
(found in Table 4.1 below). First, atrium shows have consistently less popular artists than events on the main stage. This would be expected given the pricing differences, as well as the capacity differences. The second aspect of the index is depicted by the change in maximum value and lack of change in minimum value. This shows that the most popular artists, performing at either stage, sell to maximum capacity before the night of the event.
The decrease in range of values for door-sales indicates events are not included in the subsequent dataset because there are no available tickets.
TABLE 4.1
RANGE OF POPULARITY INDEX FOR ATRIUM AND MAIN STAGES
Min Max
AtriumPreSale 0.77775 22.84183
AtriumDoor 0.77775 16.78434
MainPreSale 2.46446 42.64503
MainDoor 2.46446 38.05266
The second descriptive variable pertaining to the musicians in this study is labeled
Years. It is a measurement of the number of years, since 2009 that artists released their first commercial albums. It should be made clear the first album release referred to here is the first of the artists under the same name as they headline on stage at The Catalyst, not the first album any of those artists could have released under a pseudonym. The objective 35 of this variable is to measure the length of different musicians' careers. An artist's first major release if often associated with a first major tour and also an overseeing record label. This variable, expressed in years, portrays how long the public has potentially benn exposed to a given artist. Further proof of differences between events held on the atrium or main stage is found in the average value of the Years variable. The main stage has artists with an average of 14 years exposure, while the atrium stage has artists with an average of 7 years exposure.
The final numerical variable measures the creative output of musicians.
Productivity is the total number of major album releases by an artist divided by Years,
the number of years since the first major release (see equation 4.4 below for formula).
This quotient portrays the number of major albums released per year of a professional
musician's career. If the variable Years expresses the duration of an artist's professional
exposure, Productivity depicts the magnitude of that exposure. Majoralbum releases are
defined here as professionally produced studio or live albums with, most often,
previously unheard material. Excluded are compilations, appearances with other groups,
appearance under other names, and, in the case of hip-hop and electronic music, remixes
of previously released material. The data does not show major differences between
productivity levels for artists appearing on the main stage or in theatrium; however, the
average for data in the main stage regression is slightly higher, by about 0.2 albums/year
active.
# of Major Releases Productivity # of Years Since First Release . „ 36
The indicator variables included at the end of the regression classify, albeit generally, the style of music played during each event at The Catalyst in 2009. The rather subjective labeling of artist genre is only feasible through use of first-hand information from the artists themselves. This data is derived off of artists' MySpace web pages where there is a place for musicians to describe the music they are creating. This information must be regarded as direct from the artists, as was mentioned before, because they, or their associated parties, are presumed caretakers of all the information provided on the site. After addressing all of the descriptions of music, every event in 2009 is consolidated
into combinations of thirteen possible genre categories. These categories are rock, metal
(or hard rock), reggae, pop, hip-hop, folk, punk, rhythm and blues, funk, experimental,
rockabilly, acoustic, and world. To further generalize with the hopes of finding
significance in this study, the last four categories are grouped together into a bracket
labeled 'other' because of their sporadic occurrences. The categories are then formed
into indicator variables for entry into the study. Different than other indicator variables in
this study, where there is only one applicable indication, any given event can register
with a combination of genres. For example, if a band describes itself as a reggae hip-hop
act, it would register withboth the reggae variable and the hip-hop variable. This also
applies in a similar way to any act that was acoustic. Since the acoustic variable is
subsumed by the 'other' variable, any act that was acoustic as well another genre would
register with that genre, in addition to the 'other' genre. This is an attempt to relate to the
public's response to music type, where a lover of the reggae genre may attend a reggae
hip-hop show in the hopes of finding something familiar to the reggae sound. 37
Similar to how booking staff at The Catalyst take into account the day of the week and month of the year when scheduling events, it should be acknowledged that booking staff also consider the types of music being performed at The Catalyst. They are trying to maximize sales at all times, so theyonly pursue artists they believe people from the region have a demand for. This study hopes to find, regardless of Catalyst staff efforts, whether there are some genres out of those offered, which still sell better at The Catalyst than others.
The next chapter uses the data and method outlined above to execute a tobit regression analysis. The tests run and the results of significant variables are reported.
Chapter six, in conclusion, provides an interpretation of the regressions, as well as a consideration of the study's limitations. Finally, implications of the study and future studies are discussed. CHAPTER V
RESULTS
This chapter provides a detailed summary of all the regressions run in this study.
First, it presents the tests and adjustments made in order to increasethe predictive power of the regressions. Second, an overview of the end results of the four regressions is offered. Finally, a more detailed investigation of each variable's interaction with the dependent variable of attendance is presented.
Multicollinearity
All of the independent variables included in this study aretested against each other for multicollinearity. The purpose of this step is to make sure, as best as possible, that each variable contributes significant information to the regression independent of any other variable. In the case of two independent variables correlating strongly, the exact results of those variables in the regression lose accuracy, but the regression as a whole does not suffer. One potentially destructive correlation between independent variables exists in this dataset; occurring between Popularity and the variables expressing Price in the main stage regressions. Popularity is positively correlated with PriceBefore by approximately 56.5% in the MainPreSale regression and with PriceDoor by about
41.5% in the MainDoor regression. This is not completely unexpected and actually
38 39 shows that staff at The Catalyst price events according to some level of perceived popularity among the public. The result of this correlation, however, may diminish the potential significance of either variable in the context of its respective regression. Since there is not an issuein the atrium regressions, the significance of the popularity variable may be inferred there.
In a similar fashion as the correlation above, the AtriumDoor equation has a
correlation between Years and PriceDoor that is enough to report. The two variables are
positively correlated by about 42%, meaning that for events in the atrium, the older artists
are more likelyto have higher ticket prices than newer ones. Since the correlation is
relatively small and there are no issues with these two variables in theother regressions,
no adjustments havebeen made. Like the above situation, the significance of these two
variables must be considered across all four regressions, since the result of this particular
case may be affected.
The rest of the reported correlations (listed in Appendix C),are the result of
indicator variables, event booking, and naturally occurring phenomena outside the control
of this study. For example, the indicator variable FEB and Rainfall are correlated in
every regression by about 50%. This is because it is more likelyto rain in the month of
February for California. Different genres also correlate with different months of the year
because of how event bookings at The Catalyst transpire. There are also correlations
between day of the week or month and start time, as well as between ticket prices and age
restrictions. These are all a result of The Catalyst booking decisions. Indicator variables
like those describing the age restrictions ofeach show also have high negative
correlations because, if it is not one type of show, it must be the other. Other than the 40 significant correlations mentioned first, theseother relationships must be taken as given, since only a greater number of observations would help to diminish them.
Other Regression Tests
In addition to testing formulticollinearity, the error terms of each regression are tested for normality. Both atrium stage equations and the main stage pre-sale equation do
not show any issues of skewness or kurtosis of the error terms. The door-sale regression
of the main stage, however, shows reasonably high levels of Kurtosis, sothe null
hypothesis of normality cannot be rejected. This is believed to have happened because of
the fewer number of observations contained within the MainDoor regression compared
to the others. Unfortunately, nothing can be adjusted and the final coefficientsfor this
regression must be carefully interpreted, since there is an issue with the distribution of the
error terms.
The final adjustment of the tobit regressions run in this study also relates to the
error terms, but this time fixes the variance instead of the distribution. All of the
regressions are runwith robust standard errors in order to quell the risk of
heteroskedasticity within the error terms. This way it can be assured there is no
unexplained, random variance of the error terms, potentially reducing the predictive
power of the model.
Before estimating the demand equation, one out of each set of indicator variables
is dropped in order to create a base case. LATE becomes the base case for start times,
SIXTEEN for age restrictions, APR for months of the year, and THU is removed from
daysof the week. Since these variables are the base case, all theother indicator variables' 41 coefficients are to be interpreted in relation to them. HOLIDAY and the variables describing genre do not require a dropped variable since HOLIDAY stands alone and the genre classifications do not have a base case.
Estimations Of Demand
First and foremost, all four regressions return withhigh F-statistics signifying
that, on the whole, the equations provide a certain level of predictive power. The reported
R2 statistics, representing the percentage of variation in the observations explained by the
independent variables, conversely, are not as thrilling. The highest R2 value returned is
13.9% for AtriumPreSale, and the lowest is 9%, occurring in the MainDoor equation.
The low R2 values and high F-statistics show that, although the variables included
represent the data well, there are other factors potentially impacting ticket demand not
part of the current equations.
One more aspect of the tobit estimation procedure must first be mentioned before
proceeding on with a discussion of each independent variable. In order to accurately
interpret the impacts of each independent variable on the dependent, tobit estimation
requires an adjustment of the coefficients to give a reading of marginal effects. This
allows for the direct comparison of values between independent and dependent variable.
Fortunately, for all of the independent variables included in this study, the coefficients for
both the original estimation, as well as the one adjusted for marginal effects, read the
same. For this reason, only the coefficients of the original tobit regression are included to
cut redundancy. Refer to tables 5.1 and 5.2 for complete results of each of the four
equations. 42
TABLE 5.1
PRESALE REGRESSION RESULTS
61 Observations AtriumPreSale MainPreSale 84 Observations t-stat Coefficient Significance SoldBefore Significance Coefficient t-stat 2.31 2.14604 ** PriceBefore *** 3.45840 3.56 1.05 0.07695 Onsale ** 0.19434 2.03 1.84 1.40132 * Popularity 0.54796 0.71 1.19 0.55253 Years -0.63298 -1.45 -0.04 -0.20193 Productivity 22.62350 1.78 -1.36 -10.00575 Rainfall 9.36587 0.78
— — HOLIDAY 1.81563 0.08 -0.40 -3.37751 EARLY -2.08708 -0.17
0.06 0.67301 ALL — — -2.51 -19.60381 ** TWENTYONE 1.52343 0.12 -0.36 -5.00368 JAN 21.39439 1.09 -0.71 -8.61650 FEB -20.13513 -1.08 -1.97 -19.49186 * MAR 16.65103 0.84 -2.33 -25.21967 ** MAY 6.72846 0.35 -1.27 -12.83732 JUN -11.53427 -1.00 -1.78 -21.17002 * JUL -0.02974 0.00 -2.42 -23.64530 ** AUG -23.60967 -1.55 -1.71 -18.38067 * SEP -12.10895 -0.62 -0.81 -8.28737 OCT -2.70261 -0.16 -0.62 -7.93803 NOV 1.51742 0.11 -1.04 -9.42071 DEC -31.83515 -1.63 2.03 21.82975 * MON 0.01518 0.00 -0.06 -0.57500 TUE -14.83061 -0.94 0.53 4.54160 WED 14.50063 1.11 2.70 14.76988 ** FRI -5.90513 -0.38 -0.01 -0.04451 SAT -0.80164 -0.06 -1.91 -13.52304 * SUN -21.62193 -1.55 -0.99 -6.29265 ROCK 1.24353 0.11 -0.28 -2.41344 METAL ** 45.98741 2.22 1.62 6.11961 REGGAE ** 32.95490 2.56 -0.58 -3.43268 POP 8.93437 0.40 -0.59 -3.18541 HIPHOP ** 29.99465 2.10 0.16 0.87056 FOLK 3.67879 0.23 -0.43 -4.27874 PUNK ** 34.17327 2.63 0.13 0.73341 RNB 21.43749 0.99 -0.29 -2.65420 FUNK -21.92809 -1.41 -0.26 -1.71118 OTHER *** 36.26841 2.72 -0.21 -3.19134 Constant ** -49.65014 -2.30
20.84000 *** F-Stat *** 5.35000 0.13920 R2 0.13460 *=10% Significance Level *=5% Significance Level **=!% Significance Level 43
TABLE 5.2
DOORSALE REGRESSION RESULTS
60 Observations AtriumDoor MainDoor 54 Observations t-stat Coefficient Significance SoldDoor Significance Coefficient t-stat 0.29 0.21867 PriceDoor 0.67684 1.02 -0.09 -0.00771 Onsale ** 0.12056 2.77 0.77 0.65523 Popularity 0.18951 0.29 1.32 0.84047 Years 0.02241 0.05 -0.46 -2.96353 Productivity -8.40830 -0.85 -1.83 -12.87121 * Rainfall 7.66285 0.86 ** -47.19366 -2.20 — HOLIDAY
-0.22 -1.81341 EARLY -9.57581 -0.69
1.33 15.53690 ALL ... — -3.21 -25.51346 *** TWENTYONE -12.77452 -1.25 1.54 26.94217 JAN 24.11129 1.84 0.51 7.63089 FEB 11.95306 1.15 -0.72 -9.53101 MAR 28.49770 1.41 -0.42 -6.31076 MAY 24.67817 1.57 0.48 7.24038 JUN -7.53805 -0.65 -0.54 -6.13475 JUL ** 51.63895 2.75 -0.49 -5.57150 AUG 9.99385 0.65 0.60 9.17037 SEP 12.28200 0.93 0.73 10.01274 OCT -2.59189 -0.21 0.90 15.09097 NOV 8.95087 0.85 0.69 9.41648 DEC 11.60914 0.79 0.74 12.27930 MON ** 62.43642 2.32 -0.93 -9.51227 TUE 16.80549 1.16 -0.21 -1.53410 WED 8.48322 0.80 3.78 18.60982 *** FRI -10.00143 -0.82 -0.22 -1.24549 SAT 11.59781 0.82 -1.25 -8.02513 SUN -9.98742 -1.07 0.21 1.68607 ROCK -17.16332 -1.72 -1.16 -9.41178 METAL -5.02568 -0.25 1.63 9.27893 REGGAE 10.09875 1.00 0.29 2.16540 POP 11.40181 0.71 -0.11 -0.50980 HIPHOP 4.99035 0.37
* -0.36 -2.22985 FOLK -27.52554 -1.74 1.53 20.10790 PUNK 7.99839 0.84 2.23 14.06468 ** RNB 2.48321 0.18 -0.04 -0.30429 FUNK -0.31222 -0.03 -0.13 -0.92130 OTHER 9.25413 0.99 0.17 3.69781 Constant -1.01243 -0.05
9.71000 F-Stat 14.65000 0.11230 0.08970 *=10% Significance Level *=5% Significance Level *=1% Significance Level 44
Significant Variables - Atrium
The estimation of AtriumPreSale returns eleven significant variables: five at the
5% level of significance and six at the 10% level of significance. Of the numerical variables, only PriceBefore and Popularity are significant: the first at a 5% level of significance, the second at 10%.Both show a positive relationship with ticket demand, although PriceBefore slightly more so than the other. Elsewhere, the regression shows a large negative relationship between the dependent variable and the indicator variable for
21+ events. This suggests The Catalyst is selling significantly less tickets for events in the atrium that do not allow anyone under the age of 21. The only other variables in this particular equation, with t-statistics implying significance, are the indicator variables representing the months of the year and the days of the week. It seems the months of
March, May, July, August, andSeptember have a negative impact on ticket demand when compared to the base case of April. It also appears events on Monday and Friday are positively related toticket sales and Sunday events are negatively related.
Analysing the AtriumDoor equation, there are only four variables showing significance: two at the 1% level of significance and one each at a 5% and 10% level of significance. The only numerical variable found to be significant in this equation is
Rainfall. It shows there is a negative impact on ticket sales at the door when it rains on the same day. Just as in the pre-sale regression for the Atrium, the indicator variable
TWENTYONE shows a strong negative relationship with door sales. The variable FRI, contrastingly, comes back as positively related to ticket sales. Finally, the indicator variable for artists classified as RNB shows a positive relationship with a coefficient of
14, significant at a 5% level. 45
Significant Variables - Main
When examining the tobit estimation for MainPreSale, only numeric variables and those variables pertaining to genre are found to be significant. As in the
AtriumPreSale regression, PriceBefore shows a similar, positive relationship with tickets sold. The smallest marginal relationship with ticket demand in this equation is the variable OnSale. With a 5% level of significance, this variable has a coefficient of 0.19.
The final numerical variable relating to the pre-sale of tickets is Productivity. It has a large coefficient of 22.6 and shows the more an artist is creating, the more likely they are to sell tickets.
Exactly half of the indicator variables addressing genre are found to be
significant. These are METAL, REGGAE, HIPHOP, PUNK, and OTHER.
Interestingly, they all show a strong positive relationship with some potentially stronger than others. HIPHOP has the smallest coefficient of 30 and METAL is found to have the largest coefficient of 46. All are found to be significant at a 5% level or better.
Finally, the constant in this particular regression is found to be significant with a very
large, negative number. The interpretation of the constant in this study, however, is not
necessary because it requires all other variables to equalzero and, sincethere is an
indication for day and month, such a situation would never occur in real life.
The final regression run in this study, MainDoor, is found to have six significant
variables. The first, similar to the pre-sale regression, is OnSale. In this equation it has an
even smaller coefficient, 0.12. The second variable of significance is HOLIDAY,
indicating that events on the same day as a federal holiday potentially lose close to half of
potential sales at the door. The months of January and July show importance in this 46 particular regression: July having a large coefficient of 51.6 and January about half that with 24.1. MONDAY has the largest significant coefficient of all four regressions with a value of 62.4.Finally, the genre class of FOLK is the only one of its kind to show significance in this equation: this time reporting a strong negative relationship with door- sales.
Even though only some of all the variables are found to be significantin the regressions performed, there is substantial information to be uncovered through more in- depth reflection. The following chapter provides a summary and interpretation of the
results above to bring greater clarity to this study. Implications of the results, as well as
limitations and areas for future studies are also discussed. CHAPTER VI
CONCLUSION
The intention of this study is to provide venue managers, booking agents, and performing musicians insight into the factors contributing to the success or failure of the live music events they provide the public. Although not all tested variables are found to be significant, there is substantial information to be gathered from this study, bringing greater understanding to popular music demand, as well as strengthening subsequent studies. This chapter, first, reviews the limitations of the study before fully discussing the interpretation and implication of the study as it relates to the business decisions of staff at
The Catalyst. Finally, future areas of research are considered with closing remarks.
Limitations
The greatest limitation of this study is the relatively small dataset. Time constraints reduce the capacity for a more encompassing approach, which would use multiple years of ticketing information to provide a stronger collection of observations.
This would reduce some of the correlations negatively impacting the significance and interpretation of variables, in addition to providing greater perspective on the long-term success or failure of artists performing at The Catalyst. Many musicians perform at The
Catalyst on a regular basis, so a dataset including multiple years of observations of the
47 48 same artists, as well as some rotating artists, would bring greater understanding to changes in event context. Multiple years of data could also strengthen the examination of month and day indicator variables, since some have only a few occurrences that are likely distorting final coefficients. The variables describing the artists performing would also take into account the changes in popularity levels and productivity levels that occur over multiple years as artists grow and evolve according to their reception by the public.
Another limitation involves the measurement of popularity and reception of music
by the public. MySpace friend count is used as a way to poll thepublic's perception of an
artist, but by using only one website, there is an inherent weakness. There are, naturally,
certain types of people who are inclined to use online social networking sites and those
who are not. By limiting this study to MySpace data, there may be a skewed perspective
of popularity to a certain genre or certain age group. A bandfrom 30 years ago, with a
strong fan base consisting of older individuals, may be more popular than a younger
band, but because the older band is not as present online, the popularity measurement
could portray the younger band as more popular. No consistency of this scenario is found
in the data, however, since the Years variable is not proven to be both positive and
significant.
The manipulation of the variable Popularity might also contribute to its lack of
significance within the final regressions. As was discussed in chapter two, there are
multiple ways for studies to measure online activity. The simple transformation to take
place in this study may not be the most adequate way to use friend count as a measure of
online activity. Future studies focusing on social networking sites may help illuminate
this issue in the future. 49
The classification of genre in this study is another point of imperfection. Even though the grouping of music is taken first-hand, through information provided by the artists themselves via online sources, there is still the possibility for discrepancies in descriptions. Music today invariably has elements of many different styles, so trying to reduce classification to even a few types is difficult. Second, humans naturally experience and connect to music differently, so there is always the possibility for varying opinions.
Complications are bound to arise when there is an attempt to find the middle ground of every audience member's perspective as it relates to the decision to attend or not.
A general limitation to using the booking data of a single venue, as is the case in this study, is the implied biases the management of The Catalyst embeds into the dataset.
There are preconceived notions about what the most profitable approach to event planning is and also, to what types of music are well received in Santa Cruz. Over the history of The Catalyst, bookingand management has adapted to the population of Santa
Cruz, so there is some question to the transferability of the conclusions in this study to other venues across the nation. The main objective of this paper, however, is not to formulate universal conclusions, but rather to find meaning in a small case study that is informative to the staff at The Catalyst and provides a framework for future studies.
Interpretation
The process of interpreting the results ofeach regression must be done carefully.
Since there are limited observations and certain correlations existing because of it, every significant variable should not instantly be thought to accurately describe the real demand for tickets. This section takes cautious consideration of every variable, as it relates to the 50 regressions, in order to discover which are to be taken seriously and which are
inconclusive.
The variable PriceBefore in both the AtriumPreSale and MainPreSale
regressions is found to be significant as well as positive. Upon first glance, this could be
interpreted as the demand for tickets being inelastic, but that is not necessarily the case.
As was mentioned earlier, booking decisions at The Catalyst are a natural part of the
dataset and, subsequently, the prices set for shows are a reflection of those decisions. The
positive relationship between ticket demand and price reveals that pricing judgments by
The Catalyst are, generally, successful. The booking staff is pricingthe more attended
events higher than those less popular as a way to maximize profits without diminishing
the public's overall demand. The larger coefficient for PriceBefore in the main stage data
likely indicates the range in price of tickets is larger than that of the atrium data. Looking
back at the table of descriptive statistics in Appendix B, this is true. Interestingly,
PriceDoor for either door-sale equation is found to be insignificant, possibly indicating
that prices being set at the door are not quite in line with public demand.
The duration tickets are on sale, represented by Onsale, appears to playa role in
ticket demand for main stage data, butnot for atrium data. Although it is a small
coefficient for both pre-sale and door-sale regressions of the main stage, it is enough to
say there is no reason for booking staff to withhold the sale of tickets once an event has
been scheduled. Itis not completely clear why no significant relationships exist within
the atrium data. An answer to this question would be found in an investigation of the time
periods when the most tickets are sold for atriumshows. Unfortunately outside the realm
of this study, it would provide some insight of the issue if it were discovered that a 51 majority of atrium tickets are sold closer to the night of an event. If this is the case, it would matter less if booking staff at The Catalyst hold off on the sale of tickets for the atrium until closer to the day of a show.
The variable formulated to represent popularity of artists is not proven to be significant other than in the AtriumPreSale regression, but the correlations found between it and those variables representing price in the main stage data could be weakening the results. It does, however, make sense for popularity to playa significant role in the atrium data because, compared to the lesser-known actsthat often play there, a
single, well-known artist could skew the data. Looking back at the descriptive statistics,
the highest value of the popularity index fox AtriumPreSale is far beyond even three
standard deviations of the mean and, additionally, it is the only show to sell out of any in
the atrium data. This is most likely the cause for its positive relationship to ticket
demand, so the overall interpretation ofPopularity must be taken lightly.
The last numerical variable of significance describing artists is Productivity in
the MainPreSale regression. A possible explanation is that productive artists are arguably
more visible to the public because of their larger offerings of music, which in turn
contributes to them successfully selling tickets before an event. Once again, an
investigation of the range of the Productivity variable in Appendix B shows the
maximum value is far beyond three standard deviations. The range is half the size in the
MainDoor dataset, so significant portions of the most productive artists also have sold
out shows. It appears that market visibility and productivity can successfully contribute to
ticket sales prior to an event. 52
As predicted by the employee at The Catalyst, Rainfall and HOLIDAY have a negative impact on ticket sales at the door. In this study, however, Rainfall is only found to be significant in the atrium data and HOLIDAY is only significantin the main stage data. One reason is there are no events taking place on a holiday in the atrium,sothe variable is never included. Rainfall is potentially not a factor in the main stage data because so many shows sell out prior to door-sales and no pre-sales are likelyto suffer from future inclinations of rain. It makes sense for rainfall to playa factor in the sales of tickets at the door of the atrium, though, since it is the stage most visible from the street and likely has significant door activity on weekend nights. Holidays negatively impact door sales for the main stage because people might be less inclined to walkup to a show on a holiday compared to the individuals who buy tickets for a holiday in advance and have already decided on their evening activity.
One of the most indicative variables of significance in the regressions relates to age restriction. Although not appearing in the main stage data, there is a consistently large, negative impact of 21 and older showson ticket sales in the atrium data. This shows that The Catalyst is losing significant ticket sales to events in the atrium when there is a 21 and over age restriction in place. A suggestion to staff at The Catalyst is to reconsider the reasoning behind having 21+ shows and potentially open more shows to younger audience members.
The highest number of significant variables indicating month occur in the
AtriumPreSale data. This either means the base case of April sold amazingly well, causing the rest of the months to have a negative impact, or it might be a discrepancy in booking consistency in the atrium stage that does not occur in the main stage data. 53
Although coefficients for January and July are both significant and large in the main stage door-sales data, there are only three shows in January and two shows in July. If those few shows sold especially well, which they probably did, it is reflected in those two months having a large coefficient. Also, it must be remembered that there are abouta third fewer observations in the MainDoor regression, contributing to the bloating of
coefficients.
A similar issue arises in the MainDoor regression as it relates to Monday shows.
There are only three observations on Monday for MainDoor that presumably sold very
well, which is why there is an very large coefficient of 62. Again, in the AtriumPreSale
data, there is only one observation on Monday, so the same conclusion should be made.
Fridays, on the other hand, actually appear to make animpact on atrium ticket sales. In
both the pre-sale and door-sale regressions, there is a considerable positive relationship
between ticket demand and Friday events. The atrium appears to be a place for weekend
activity only slightly dependent upon the music, while the main stage attracts individuals
looking to attend music they enjoy, regardless of the day of the week. The variable
indicating Sunday events fox AtriumPreSale regressed as negative, whish is most likely
due to a combination of the above factors; the atrium being a weekend activity and there
being only three observations for that day of the week in the dataset.
The interpretation of indicator variables regarding genre classification of artists to
perform at The Catalyst is somewhat difficult. Mentioned previously, the classification
system is rather subjective. Since it is using the perspective of the artist performing the
music, there is a potential for audience members to perceive the music as different than it
is defined here. Regardless, interpretation of the significant variables must be addressed. 54
It appears the best selling acts on the main stage, according to the pre-sale dataset, are metal, reggae, hip-hop, punk, and 'other' (the most common within the 'other' group being acoustic and world music). This gives some perspective on the types of music consistently selling well at The Catalyst. The variable for rock music, in these regressions, likely suffered because of the frequent use of rock as a description by artists
of a wide variety of music. The two other genre variables of significance, outside of the
MainPreSale data, are RNB in the AtriumDoor regression and FOLK in the MainDoor
regression. There is some ambiguity here because these variables occur six and five
times, respectively. There is a potential for them to be influenced by a lack of frequency
or they might be telling of the reception of rhythm and blues music and folk music. If it is
indeed indicative of the reception to either genre, it appears rhythm and blues music is
received well with individuals attending atrium events and folk music is not received as
well by main stage attendees.
Future Research And Final Considerations
The approach formulated in this study is intended to be both replicable and
applicable to additional studies regardingattendance of popular music events in a wide
range of contexts. The ability for business operators to imitate this study will bring
greater understanding to the demand characteristics of their individual venues. The
facility to find even the simplest tendencies, such as the indication that one stage sells the
best on Fridays, enables business operations to grow and adapt according to evolving
market trends. Future studies incorporating a broader range of venue types, investigating
both small capacity music clubs and large stadium venues will help to create a larger, 55 more comprehensive collection of studies, giving perspective to the overarching, national demand trends of live popular music.
This study opens the possibilities for an assortment of future studies as they relate toticket demand. The greatest opportunity lies in the investigation of demographic and regional audience demand. As was found in this study, age can playa significant factor in demand, but there may still be other impending determinants of demand. Subsequent research can build and expand on the structure of this study with the incorporation of more variables pertaining to audience members. This would positively influence the marketing strength of venue operators, as they may uncover the greatestlocation, the perfect age bracket, orthe right culture near their venue with latent demand for certain types of music. Further analysis of music genre as it then relates to these audience factors will also strengthen subsequent studies.
Further analysis of demand over a broader span of time will help to uncover significant trends in the demand for music as it evolves alongside the development of musicians' careers. Stronger incorporation of variables describing the performing musicians would also be beneficial. Continued investigation of the relationships between online, social activity and the demand for music is another large area for future study as the knowledge of these online sources continues to develop.
Substantial to this study, and something necessary to future studies, is the reliance on communication between experienced industry players and researchers. As was the case for this investigation, development of a model incorporating factors believed to
exist, as well as those known to exist, creates greater unity and application between 56 scholarly research and actual business practice. This helps to maximize the utility of studies, so they are not disjointed works with little or no function in the music industry.
Live performance is going to be paramount to the survival of music culture in the coming decades. With music consumption at an all time high and profits from music at an
all time low, there is an imminent change in dynamic likely to occur to the music
industry. This study, for the first time, examines the potential determinants of live music
demand in order to gain perspective of these changing dynamics. APPENDIX A
TABLE 7.1
DEFINITION OF VARIABLES
Dependent Variables Definition % of tickets sold out of those available either online SoldBefore or at the box office up until 3 hours (average)before the start of the event % of tickets sold out of those available at the box
SoldDoor office the night of the show: starting3 hours (average) before the start of the event Independent Variables Price of ticket ($) before the night of the event: NOT PriceBefore including convenience fees or CA tax Price of ticket($) the night of the event: no PriceDoor convenience fees, CA tax NOT included # of days tickets are available for purchase prior to Onsale the event through ticketing website and box office The square root of a band's total # of MySpace Popularity friends divided by the total # ofdays since they first formed a MySpace website (collection date 1-25-10)
Years # of years since a band's first major album release Total # of major albums produced divided by 'Years' Productivity variable (albums per year) Total accumulated rainfall, in inches, of any given Rainfall day of the year- place of measurement approximately 2.5 miles from venue IndicatorVariables 1 if event falls on a Federalholiday or Cesar Chavez HOLIDAY Day (NOT considering holiday weekends), 0 if event is not on a holiday EARLY is defined as 8:00 PM or earlier, LATE is EARLY/LATE 8:30 PM or later (Doors open, event starts probably an hour later)
ALL/TWENTYONE/SIXTEEN Age requirement of an event: 21+, 16+, or all ages
JAN/FEB/MAR/APR/MAY/ In which month the event takes place JUN/JUL/AUG/SEP/OCT/NOV/DEC MON/TUE/WED/THU/FRI/ On what day of the week the event takes place SAT/SUN ROCK/METAL/REGGAE/ Genre classifications of each performance, as FUNK/POP/HIPHOP/FOLK/PUNK/ defined by a group's MySpace website RNB/OTHER
57 APPENDIX B
TABLE 7.2
DESCRIPTIVE STATISTICS OF NUMERICAL VARIABLES
AtriumPreSale Variable Observations Mean Std. Dev. Min Max SoldBefore 61 17.55035 19.42048 0.286 100.000 PriceBefore 61 9.08525 3.55105 3.000 22.000 OnSale 61 59.80328 27.46805 13.000 179.000 Popularity 61 7.74280 3.89241 0.778 22.842 35.000 Years 61 6.65574 7.38892 0.000 Productivity 61 0.49536 0.35664 0.000 1.500 Rainfall 61 0.08131 0.24966 0.000 1.510
AtriumDoor Variable Observations Mean Std. Dev. Min Max
SoldDoor 60 23.65123 19.83802 1.441 100.000 PriceDoor 60 12.26667 3.70920 6.000 26.000 OnSale 60 59.85000 27.69741 13.000 179.000 Popularity 60 7.49115 3.38810 0.778 16.784
Years 60 6.61667 7.44492 0.000 35.000 Productivity 60 0.48695 0.35350 0.000 1.500 Rainfall 60 0.08267 0.25154 0.000 1.510
MainPreSale Variable Observations Mean Std. Dev. Min Max SoldBefore 84 65.45000 31.92463 6.100 100.000 PriceBefore 84 20.08321 5.93633 12.000 49.000 OnSale 84 62.25000 31.91777 14.000 242.000 Popularity 84 16.62286 7.99135 2.465 42.645 Years 84 13.52381 10.18678 0.000 45.000 Productivity 84 0.66933 0.42719 0.000 3.000 Rainfall 84 0.07548 0.27224 0.000 1.510
MainDoor Variable Observations Mean Std. Dev. Min Max
SoldDoor 54 25.04547 22.65561 2.067 100.000 PriceDoor 54 23.31481 5.24791 15.000 42.000 OnSale 54 64.00000 36.92726 14.000 242.000 Popularity 54 15.18909 2.46446 2.465 38.053
Years 54 15.62963 11.09359 0.000 45.000 Productivity 54 0.59317 0.32280 0.000 1.667 Rainfall 54 0.09926 0.32166 0.000 1.510
58 59
TABLE 7.3
FREQUENCY OF INDICATOR VARIABLES
Variable AtriumPreSale AtriumDoor MainPreSale MainDoor 2 HOLIDAY _ - 2 EARLY 8 8 46 31 LATE 53 52 38 23
ALL 4 4 - - TWENTYONE 8 8 19 14 SIXTEEN 49 48 65 40 JAN 5 5 6 3 FEB 8 8 10 10 MAR 5 5 7 5 APR 6 5 10 6 MAY 8 8 9 3 JUN 5 5 2 2 JUL 4 4 2 2 AUG 6 6 6 4 SEP 6 6 9 6 OCT 4 4 10 5 NOV 2 2 9 5 DEC 2 2 4 3 MON 1 1 3 3 TUE 5 5 8 7 WED 8 8 10 4 THU 16 16 14 9 FRI 13 12 19 11 SAT 15 15 18 10 SUN 3 3 11 9 ROCK 31 31 32 23 METAL 4 4 7 2 REGGAE 19 19 30 20 POP 7 7 4 2 HIPHOP 9 8 19 10 FOLK 9 9 8 5 PUNK 7 7 16 6 RNB 6 6 4 3 FUNK 4 4 5 4 OTHER 14 14 14 6 APPENDIX C
TABLE 7.4
REPORTED CORRELATIONS
AtriumPreSale AtriumDoor TWENTYONE x PriceBefore 0.4043 Years x PriceDoor 0.4199 SIXTEEN x PriceBefore -0.4330 TWENTYONE x PriceDoor 0.4781 THU x PriceBefore -0.4165 SIXTEEN x PriceDoor -0.4509 FEB x Rainfall 0.5020 THU x PriceDoor -0.4126 LATE x EARLY -1.0000 FEB x Rainfall 0.5010 ALL x EARLY 0.4857 EARLY x LATE -1.0000 ALL x LATE -0.4857 ALL x EARLY 0.4848 JUN x EARLY 0.4150 ALL x LATE -0.4848 JUN x LATE -0.4150 JUN x EARLY 0.4231 WED x EARLY 0.4245 JUN x LATE -0.4231 WED x LATE -0.4245 SIXTEEN x ALL -0.5345 SIXTEEN x ALL -0.5353 SIXTEEN x TWENTYONE -0.7845 SIXTEEN x TWENTYONE -0.7851 OCT x TWENTYONE 0.4848 OCT x TWENTYONE 0.4857 METAL x JAN 0.6447 METAL x JAN 0.6451 MON x APR 0.4318 FUNK x JUN 0.4037 FUNK x JUN 0.4029 OTHER x THU 0.4722 OTHER x THU 0.4693
MainPreSale MainDoor Popularity x PriceBefore 0.5651 Popularity x PriceDoor 0.4154 FEB x Rainfall 0.5468 ROCK x OnSale 0.4003 JUL x HOLIDAY 0.4878 FEB x Rainfall 0.5666 LATE x EARLY -1.0000 JUL x HOLIDAY 0.4808 FRI x EARLY -0.4805 LATE x EARLY -1.0000 FRI x LATE 0.4805 FRI x EARLY -0.4942 SIXTEEN x TWENTYONE -1.0000 FRI x LATE 0.4942 REGGAE x TWENTYONE -0.4030 SAT x EARLY -0.4571 REGGAE x SIXTEEN 0.4030 SAT x LATE 0.4571 METAL x AUG 0.4181 SIXTEEN x TWENTYONE -1.0000 HIPHOP x REGGAE -0.4030 REGGAE x TWENTYONE -0.4537 FUNK x RNB 0.4163 REGGAE x SIXTEEN 0.4537
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