AN EMPIRICAL STUDY OF THE DETERMINANTS OF THE RUN-TIMES OF BROADWAY MUSICALS
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
Katie Ferguson
May 2011
AN EMPIRICAL STUDY OF THE DETERMINANTS OF THE RUN-TIMES OF BROADWAY MUSICALS
Katie Ferguson
May 2011
Mathematical Economics
Abstract
With an economic impact of $9.8 billion in the 2008-09 season alone, Broadway as an industry which should be economically studied. Currently there is a large gap in scholarly literature about Broadway with only three quantitative studies having been performed. This thesis aims to help fill this gap by building off of these three studies to determine which factors influence the success- measured as total days on Broadway from opening to closing night- of a Broadway musical. This thesis focuses specifically on musicals as they have been shown in all three empirical studies to have longer runs and a larger economic impact than Broadway plays. The econometric analysis finds many variables- such as a movie version of the musical being released, and winning the Tony for Best Musical- that are predictive of loner run times. Revivals are found to have substantially shorter run times than original runs and over time, musicals are lasting longer.
KEYWORDS: (Broadway, Musical, Theatre, Run-time)
TABLE OF CONTENTS
ABSTRACT
1 INTRODUCTION 1
2 LITERATURE REVIEW 5 Characteristics of the Film Industry...... 5 Live Theatre...... 7 Modern Day Broadway...... 9 Empirical Studies...... 12 18 3 THEORY
4 DATA & METHODOLOGY 32
5 RESULTS AND CONCLUSION 52
APPENDIX A 65 WORKS CONSULTED 67
CHAPTER I
INTRODUCTION
Broadway makes an enormous impact on the local economy of New York City. In the
2008-09 season alone, Broadway shows and the overall industry generated $9.8 billion of impact to the local economy.1 This is broken down into types of spending in Figure 1.1 below:
FIGURE 1.1
Broadway’s Economic Impact 2008-2009
Source: “Broadway’s Economic Contribution to New York City 2008-2009”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=broadway-s- economic-contribution-to-new-york-city, (Date accessed: April 2011).
The 2009-10 Broadway season grossed $1.02 billion with a total of nearly 12 million attendees.2
1 “Broadway’s Economic Contribution to New York City 2008-2009”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=broadway-s-economic-contribution-to-new- york-city, (Date accessed: April 2011).
2 “Broadway Facts”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Broadway-Facts(6).pdf, (Date accessed: April 2011).
1
2
The Broadway League Research Department found that “Broadway attendance for the
2009-2010 season topped those of the ten professional NY and NJ sports teams combined”
(Mets, Yankees, Rangers, Islanders, Knicks, Liberty, Giants, Jets, Devils and the Nets).3 In addition, the Broadway industry supported 84,400 local jobs in 2009.4
The majority of individual spending is by tourists, whose decisions to visit New York are often based on attending Broadway shows. During the 2009-10 season, nearly 65% of all tickets to Broadway shows were purchased by tourists.5 Tourists who attend Broadway shows are both national (3.5 million people- $3.1 billion) and international (540,000 people- $2.1 billion). Together they contribute $5.2 billion to the economy just through ancillary spending while visiting Broadway shows.6
The impact of Broadway shows extends across the United States through touring productions, with touring companies traveling nationally and sometimes internationally, further extending the profits of the show through ticket sales, ancillary products and memorabilia.7 In the 2007-08 season, 15.3 million tickets were purchased to attend touring Broadway
3 “Broadway Facts”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Broadway-Facts(6).pdf, (Date accessed: April 2011).
4 “Broadway’s Economic Contribution to New York City 2008-2009”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=broadway-s-economic-contribution-to-new- york-city, (Date accessed: April 2011).
5 “The Demographics of the Broadway Audience 2009-2010”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=the-demographics-of-the-broadway-audience, (Date accessed: April 2011).
6 “Ancillary Spending by Broadway Tourists”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Report%202008-2009_SamplePage17.pdf, (Date accessed: April 2011).
7 “Touring Broadway Facts”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Touring-Broadway- Facts(9).pdf, (Date accessed: April 2011).
3 productions, most of which were musicals.8 Touring Broadway shows, both musicals and plays, grossed $947 million in the 2008-09 season, with nearly 16 million people paying for tickets to the shows. Touring shows contribute over $3.4 billion to metropolitan cities that host touring shows. On average, these tours generate impact on local economies that is 3.5 times the gross ticket sales.9 Musicals gross more than four times as much as plays on Broadway, with attendance almost five times as large.10
Previous literature and research into the arts industry has left a gap in the area of the
Broadway industry. Only a few studies (one conducted in 199811, one in 200312, and a third in
200413) have attempted to empirically determine the factors that influence the success and longevity of Broadway shows. Not one study has studied musicals independently. With
Broadway continuing to play a major role in local and national economies, it is an important process to understand - it’s time for a new study to be conducted.
With such a high risk involved in producing Broadway shows, and musicals especially, it is important to figure out how to make a show successful- that is, how to extend the run time.
Other studies have looked at audience attendance as a measure of success. However, even a
8 “The Audience for Touring Broadway- A Demographic Study 2007-2008”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=new-the-audience-for-touring-broadway-a- demographic-study, (Date accessed: April 2011).
9 “Touring Broadway Facts”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Touring-Broadway- Facts(9).pdf, (Date accessed: April 2011).
10 “Broadway Season Statistics at a Glance”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/81010Broadway%20Statistics%20at%20a%20Glance.pdf, (Date accessed: April 2011).
11 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 370-383.
12 Jeffrey S. Simonoff and Lan Ma, “An Empirical Study of Factors Relating to the Success of Broadway Shows” The Journal of Business, Vol. 76, No. 1 (January 2003): 135-150.
13 David Maddison, “Increasing returns to information and the survival of Broadway theatre productions” Applied Economics Letters, Vol. 11, (2004): 639-643. 4 sold out show cannot recoup its costs if it only runs for a week, nor will it go on to the level of national recognition that longer-running shows potentially can. It is because of this fact that it is important to empirically determine which factors affect how long a musical will run on
Broadway. This thesis aims to help to fill the gap in the academic literature by conducting a current empirical study of how various internal and external characteristics of Broadway musicals affect how long they run on Broadway.
CHAPTER II
LITERATURE REVIEW
This chapter will discuss past studies and articles relating to the field of theatre and specifically the Broadway industry. It will start with a section on the film industry and similarities found between the film and theatre industries. The next section will focus on the general field of theatre and then go on to focus on Broadway shows. The final section will discuss the three studies that provided the foundation for this thesis.
This chapter will conclude with how this study ties into the current literature on
Broadway musicals and the determinants of their run times.
Characteristics of the Film Industry
The film industry has a much wider scope of marketing and distribution than that of live theatre because filmmakers can simply send the same pre-recorded film all over the world and easily make profits from it. In contrast, theatre productions are typically localized in one theatre. Even if the show is toured, it is still generally only performed in one theatre at a time, with costs increasing with each subsequent show.
Scott’s article on motion picture marketing and distribution discusses factors that lead to different popularity and success of films.1 He goes on to discuss factors that are not relevant to live theatre producers, such as the difference in how success is determined.
1 Allen J. Scott, “Hollywood and the World: The Geography of Motion-Picture Distribution and Marketing” Review of International Political Economy, Vol. 11, No. 1 (Feb. 2004): 33-61.
5
6
Films aim for recognition and profits by distributing the product as far and wide as possible, since there is minimal ongoing production cost associated with doing so. The same distributor can disperse one film all over the world and recoup the profits. Film producers make deals with theatres to get their film launched initially. Then after a period of a few weeks to a few months, their focus changes from distributing to movie theatres, to advertising to consumers to get them to purchase the DVD and other associated products. Films try to build a brand that will carry on long after the movie is out of the theatre. Many theorists, including Brooks McNamara, talk about the similarity in profit seeking behavior in the film and theatre industries. Both are occupational districts and therefore must make a profit to survive. 2
The film industry is analyzed as an oligopoly3 of seven or eight distributors; so are Broadway theatres, which are mostly managed by five owners.4
Similar to live theatre, the film industry experienced a drop in profits and interest in the 1960’s and 1970’s, when live television became more easily available to the normal consumer.5 Around this same time, Edney states that the structure of musicals began to change away from the traditional musical comedies that first rose to prominence in the 1920’s and 30’s.6
2 Brooks McNamara, “A Theatre Historian’s Perspective” The Drama Review , Vol. 45, No. 4 (Winter, 2001): 125-128.
3 William Baumol, “On the Theory of Oligopoly” Economica, Vol. 25, No. 99 (August 1958): 187-198.
4 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 453-460.
5 Allen J. Scott, “Hollywood and the World: The Geography of Motion-Picture Distribution and Marketing” Review of International Political Economy, Vol. 11, No. 1 (Feb. 2004): 33-61.
6 Kathryn Edney, “Resurrecting the American Musical: Film Noir, Jazz, and the Rhetoric of Tradition in City of Angels” Journal of Popular Culture, Vol. 40, No. 6 (December 2007): 938. 7
Live Theatre
Moore talks in his article at great length about the effect the Depression, the
Korean War and World War II had on the live theatre industry - specifically Broadway and New York. When the Depression hit, far fewer shows opened in the following years; however, this often led to increased runtimes for the shows that were already opened. Additionally, with the increased population after and the need for an escape from the times that came with the wars, attendance increased through the 40’s and
50’s.7
A similar pattern can be seen following the recent financial collapse in the
United States. As theatre is seen as a normal good, purchase of tickets will drop as personal income falls. This can be seen in the studies The Broadway League conducted looking at attendance and gross profits over the last 5 seasons of Broadway shows.8
Bennett discusses that theatre and the arts have the power to reinvigorate cities, bringing in jobs and recreational activities as well as increasing tourism, both of which have been shown repeatedly by Bennett9, McNamara10 and Zoglin11 among others to have a huge positive economic impact on the cities they visit.
7 Thomas Gale Moore, “Broadway Theatre Myths” The Tulane Drama Review, Vol. 10, No. 1 (Autumn, 1965): 95-100.
8 “Broadway Season Statistics”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/index.php?url_identifier=season-by-season-stats-1, (Date accessed: April 2011).
9 Susan Bennett, “Theatre/Tourism” Theatre Journal, Vol. 57, No. 3 (Oct. 2005): 407-410.
10 Brooks McNamara, “A Theatre Historian’s Perspective” The Drama Review , Vol. 45, No. 4 (Winter, 2001): 125-128.
11 Richard Zoglin and Lisa McLaughlin, “Life After Rent” Time, Vol. 171, No. 10 (2008): 65- 67. 8
McNamara discusses in depth the commercialism of live theatre in the 1980’s and 1990’s. With companies trying to make mega-musicals and shows with huge hype and long run times, producers and creators lose sight of creating a show with high artistic value and instead try to market and cater to the mass-market consumer.
Supporting McNamara’s point, Mary Ann Fortune’s says that Broadway musicals have an appeal as pure entertainment.12
As Moore states in his “Broadway Theatre Myths”, the cost of attending a live theatre performance will always be higher than attending a film, as the cost per spectator is higher.13 As the shows are seen as experiential goods by Reddy et al.14 and
Simonoff and Ma15, it is assumed that audience members gain an incalculable utility from the show. This utility will be different to each consumer, which is why it is complicated to aim to create a mega-musical. Beggs also discusses the experience of attending a theatrical performance as being twofold: seeing the show the consumer paid for, and the experience of being in a theatre. She talks about how Urinetown was purposefully first opened in a slightly run-down theatre to add further convey the themes of the show and increase the overall experience of seeing the show.16
12 Lauren Kay, “Broadway Now!” Dance Magazine, Vol. 82, No. 10 (2008): 26-30.
13 Thomas Gale Moore, “Broadway Theatre Myths” The Tulane Drama Review, Vol. 10, No. 1 (Autumn, 1965): 95-109.
14 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 374.
15 Jeffrey S. Simonoff and Lan Ma, “An Empirical Study of Factors Relating to the Success of Broadway Shows” The Journal of Business, Vol. 76, No. 1 (January 2003): 135. 16 Anne Beggs, “’For Urinetown is your town…’: The Fringes of Broadway” Theatre Journal, Vol. 62, No. 1 (March 2010): 53. 9
McNamara also discusses the destruction of local scene shops and other local shops to make way for a more industrial mass marketing approach.17 The old arts-loving theatre audiences that used to support Broadway have transformed into business men and women impressing clients, and tourists looking to feel that they did everything there was to do in New York City.18
Modern Day Broadway
Zoglin’s article gives hope to the art lovers. He finds that following the 1980’s and 1990’s age of over-the-top commercial musicals, the end of the 1990’s and the move into the new century brought about a renewed interest in smaller artistic shows.
Additionally it piqued an interest in - and the success rate of - edgier musicals making political or social statements, such as Avenue Q and Urinetown.19 In another article by
Zoglin, he again discusses the more recent edgier and diverse musicals and how they have led to more diverse audiences. In 2007, over 26% of Broadway theatre audiences were non-Caucasian; a record high at that point. These shows often aim to get a message across to the audiences and to tell stories rather than merely sing songs.20
Theodore and Loney’s article21, Edney’s journal entry22 and Beggs’ study23 discuss musical structures specific to Broadway musicals. All three studies advocate
17 Brooks McNamara, “A Theatre Historian’s Perspective” The Drama Review , Vol. 45, No. 4 (Winter, 2001): 125-128.
18 Ibid.
19 Richard Zoglin, “The Battle for Broadway: Poppins vs. Dylan Plus Grey Gardens and Spring Awakening” Time , Vol. 168, No. 20 (2006): 137.
20 Richard Zoglin and Lisa McLaughlin, “Life After Rent” Time, Vol. 171, No. 10 (2008): 65- 67.
21 Lee Theodore and Glenn Loney, “Broadway Dancin’” Performing Arts Journal, Vol. 4, No. ½ (May, 1979): 129-141. 10 that a more traditional music structure is most successful and that often consumers’ dissatisfaction with musicals can come from a feeling of nostalgia for more old fashioned musicals.
Beggs goes on to prove that musicals containing radical political critique can still achieve commercial success. She references Urinetown as an example of this, as it is politically motivated but still went on to have a very successful run with 965
Broadway performances and over 152 university and professional productions in the
2007-08 season alone.24
Bennett finds that the theatre industry is unique in that the creation of one original show can lead to a large economic impact both directly from its time running in the local economy and indirectly through subsequent tours and revivals of the production.25 She also discusses the immediate impact commercial shows have on the local economy through spending as well as through the jobs that are created, both at the theatre itself (cast, crew, staff) and in nearby industries that the audience members will combine with their evening of theatre to create a grander experience. Reddy et al. also support this ripple effect into the local economy, analyzing Broadway shows specifically as an experiential good.26
22 Kathryn Edney, “Resurrecting the American Musical: Film Noir, Jazz, and the Rhetoric of Tradition in City of Angels” Journal of Popular Culture, Vol. 40, No. 6 (December 2007): 936-952.
23 Anne Beggs, “’For Urinetown is your town…’: The Fringes of Broadway” Theatre Journal, Vol. 62, No. 1 (March 2010): 41-57.
24 Ibid.
25 Susan Bennett, “Theatre/Tourism” Theatre Journal, Vol. 57, No. 3 (Oct. 2005): 407-410.
26 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 376. 11
In addition to ticket and memorabilia purchases specifically linked to the show itself, practically all the research finds that substantial impact of a successful Broadway shows comes from ancillary spending. Moore finds that even in the 1960’s when many ancillary options hadn’t yet been thought of, Broadway fed talent into new communities as shows toured and the soundtracks helped to sustain the record industry.27
Even in the 1960’s when Moore’s “Broadway Theatre Myths” was published, the theme of musicals being more popular than plays was prevalent. They were expected to perform better and attract larger and more diverse audiences, thereby increasing profits and run times of the shows.28 Musicals are more expensive than other shows, but this gives the consumer the sense that it is worth more as an experience and that the production value will be higher.29
Many theorists speculate that an easy way to cut costs of a show and therefore give it a better chance at a long run on Broadway is to have a smaller cast.30 This logic makes sense - hiring fewer actors cuts expenses of paying them. However, the empirical studies conducted do not find either way on this subject.31
27 Thomas Gale Moore, “Broadway Theatre Myths” The Tulane Drama Review, Vol. 10, No. 1 (Autumn, 1965): 95-109.
28 Ibid.
29 Jeffrey S. Simonoff and Lan Ma, “An Empirical Study of Factors Relating to the Success of Broadway Shows” The Journal of Business, Vol. 76, No. 1 (January 2003): 135-150.
30 Thomas Gale Moore, “Broadway Theatre Myths” The Tulane Drama Review, Vol. 10, No. 1 (Autumn, 1965): 95-109.
31 David Maddison, “Increasing returns to information and the survival of Broadway theatre productions” Applied Economics Letters, Vol. 11, (2004): 639-643. 12
Empirical Studies
Current literature on Broadway shows specifically is very sparse, with only three studies empirically measuring factors that affect success of the shows.
“Exploring the Determinants of Broadway Show Success”
In 1998, Reddy et al. conducted the first empirical study into what sorts of factors determine shows’ success. They focus on a two part system, looking at factors that affect both “Show Longevity,” or total performances, and “Show Success,” or total attendance and box office receipts.32 Reddy et al.’s study finds that musicals fare better than plays when it comes to both longevity and attendance. In fact, they also found that the decline in theatre attendance in the early 1930’s coincides with a drop in new musicals being produced.33 It was also discovered empirically that previews have a positive influence on audience attendance but do not have a significant or influential impact on run times.34 They also found that the characteristics of the key talent - including the celebrity status of participants - do not have a consistently significant effect on longevity. Ticket prices also are found not to have a significant relationship with either audience attendance or longevity, and March openers tend to have a longer run time than other months. Other empirical findings include that critics’ reviews are mixed in their influence and that they may be predictors or influencers, but this isn’t explicitly determined in this study. They suggest musicals are often more aggressively promoted than plays or other shows, which can lead to their increased popularity. Their
32 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 374.
33 Ibid, 380.
34 Ibid, 370. 13 paper discusses the risks that investors assume, as only 11% of all shows make it past
500 performances.35
Like other studies discussed earlier, this study views theatre as an experiential good, similar to the entertainment industry as a whole. Audience members are not aiming to get anything physical out of the show; rather, they spend the time and money to experience the show. This study identifies Broadway shows as being luxury goods, meaning that as income drops, so will demand for tickets. Since there is no guarantee of what the experience will turn out to be, as income drops, people are increasingly unwilling to take the risk with their money when they can find pleasure in other industries that have a more concrete expectation system. The film industry is one example of this, where previews can be found online along with entire websites dedicated to analyzing and compiling critics’ and audience members’ reviews.36 This study also briefly discusses the differences in marketing film and theatre and concludes that the film industry is sufficiently large that it is easier for them to get the word out, whereas theatre relies primarily on reviews, advertisements and word of mouth.37
Limitations on this study are in the fact that the data only spans three years and was collected decades ago, before many iconic shows of today were even conceived.
Additionally, when this data was being collected, it was difficult to access data and get reliable results.38
“An Empirical Study of Factors Relating to the Success of Broadway Shows”
35 Ibid, 371-374.
36 Ibid.
37 Ibid.
38 Ibid. 14
A few years later, in January 2003, Simonoff and Ma performed another study using the Cox proportional hazards model to find which factors had the most influence on the longevity of Broadway shows.39 They based much of their study on Reddy’s but focused only on longevity and not on audiences as a dependent variable, used a more complex model, and updated the data to more recent seasons. They focused on longevity because they found that the show’s total revenue and longevity are strongly correlated (0.943).40
They also compared the theatre industry with the film industry. Genre was found to greatly influence success of films, and was also an influential characteristic in the theatre industry. Academy Awards were found to lead to more successful films, just as
Tony awards lead to more successful Broadway shows. A fundamental difference between film and theatre, however, is that for Broadway the supply is fixed and local, whereas the film industry is much more flexible with virtually no limit on theatre space and relatively low cost in re-releasing a film after its premiere.41
As in most studies, this article explains that theatre is an experiential good, which means there is typically no need to experience it more than once or twice. This also leads to audience members wanting as much information as possible in advance.
Because of this finding, this study focused a lot of energy on getting critics’ reviews as
39 Jeffrey S. Simonoff and Lan Ma, “An Empirical Study of Factors Relating to the Success of Broadway Shows” The Journal of Business, Vol. 76, No. 1 (January 2003): 135.
40 Ibid, 137.
41 Ibid, 135-136. 15 a numerical independent variable. They used doctoral students to evaluate the “score” a critic review would have given and used this in their model.42
They felt the need to conduct this empirical study because the data in Reddy’s study was outdated and the size of the theatre and awards won weren’t taken into account. Simonoff and Ma’s empirical findings include that the type of show and whether or not it is a revival have significant effects on attendance. They also found that winning Tony awards leads to a longer run time, but being nominated for an award and losing is associated with a shorter run after the award ceremony.43 Their data was collected at www.playbill.com, allowing them to retrieve a slightly larger data set and compile more accurate results. They used a data set of Tony-eligible shows from three seasons. This study defines longevity three ways: as total performances from opening night, total performances after the announcement of Tony Award nominations, and total performances after the announcement of Tony winners. Although their data set began larger than Reddy’s study, over half the shows included closed after 10 or fewer performances, leading to a total loss for investors. Broadway musicals frequently cost up to $10 million to produce, leading to much higher risk for investors.44
42 Ibid, 138.
43 Ibid 139.
44 Ibid, 135-138. 16
“Increasing returns to information and the survival of Broadway theatre productions”
In 2004, David Maddison conducted another study including both the film and
Broadway industries finding effects of multiple variables on the number of performances a show has and the awards it goes on to receive.45
His empirical findings included that genre impacted the run time greatly, original shows traditionally last much longer than revivals, and that the majority of
Broadway shows produced are comedies or dramas. He finds, like Smirinoff and Ma, that winning an award is associated with a longer run time. Also, he discusses that over time, theatrical productions are tending to have longer run times. He raises the question of why anyone would want to present a revival as they typically have shorter run times and do not achieve as large a profit. He answers this question partially by finding that revivals, although less likely to have a long run, are also far less likely to close after just a few shows.46
Maddison takes advantage of a large database of shows not previously available or used. Like this thesis, he uses the Internet Broadway Database to get opening night information and run times for a large number of Broadway shows.47 He uses a data set that is much larger than any previously analyzed, including every play or musical recorded in the IBDB database which opened between 1960 and 2003, resulting in 1859 total shows analyzed.48 Additionally, he uses number of performances as a measure of
45 David Maddison, “Increasing returns to information and the survival of Broadway theatre productions” Applied Economics Letters, Vol. 11, (2004): 639-643.
46 Ibid, 639-643.
47 Ibid, 640.
48 Ibid. 17 success rather than revenues, which is more traditionally used in films. Maddison discusses the similarities between determinants of film success and live theatre success.
With all of these similarities, he finds it surprising that there is so much literature on the film industry and such a void when it comes to theatre, and specifically Broadway with its immense economic impact.49
Beyond these three studies, nothing has been done to directly link independent factors to longevity or success of Broadway shows. There have also been no studies specifically observing musicals, even though the literature stands together that musicals on average over a season have a much longer run time, and are much more profitable, than Broadway plays.50
This chapter has summarized much of the literature currently available on the topic of Broadway shows’ success and their findings. The following chapter overviews the theory used in this paper to determine the best methodology to find the direct effect many independent variables have upon the number of days a musical stays on
Broadway.
49 Ibid, 640-643.
50 “Broadway Season Statistics at a Glance”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/81010Broadway%20Statistics%20at%20a%20Glance.pdf, (Date accessed: April 2011). CHAPTER III
THEORY
This chapter will outline the success maximization problem Broadway producers work with. It will then go on to discuss the various factors that play a part in this maximization. The beginning of this chapter focuses on the way Broadway producers and directors can use sales maximization to accomplish longer run times and higher profits. Next, a mathematical model is outlined that may be used to test the hypothesis of sales maximization on Broadway. The second half of this chapter goes into detail about the theoretical factors affecting the longevity of the shows and how they should affect the run times. The models and variables presented in this chapter will be empirically tested in Chapter IV.
Sales Maximization
The sales maximization model proposed by William Baumol in the 1950’s aims to account for the fact that many oligopolies are more interested in maximizing sales after some adequate rate of return has been reached, rather than always seeking higher profits.1 He hypothesizes that this rate of return is a minimum acceptable profit level determined by long-run projections and plans sufficient to cover expenses of the current
1 Dominick Salvatore, “Managerial Economics in a Global Economy, 5th Edition”, Scribd, 2004, available from http://www.scribd.com/doc/3045111/sales-maximization-model, (Date accessed: April 2011).
18
19 undertakings.2 Sales - also known as total revenue (TR) - will be at a maximum when the firm produces a quantity that makes marginal revenue (MR) equal to zero.3 This is shown in the graph below:
Figure 3.1
PROFIT AND SALES MAXIMIZATION
Source: Dominick Salvatore, “Managerial Economics in a Global Economy, 5th Edition”, Scribd, 2004, available from http://www.scribd.com/doc/3045111/sales-maximization-model, (Date accessed: April 2011).
Sales Maximization on Broadway
The main goal of a Broadway producer is to put on a successful show. Although success is often associated with pure profits4, this thesis uses longevity of a show - the
2 William Baumol, “On the Theory of Oligopoly” Economica, Vol. 25, No. 99 (August 1958): 187-198.
3 Dominick Salvatore, “Managerial Economics in a Global Economy, 5th Edition”, Scribd, 2004, available from http://www.scribd.com/doc/3045111/sales-maximization-model, (Date accessed: April 2011).
4 Brooks McNamara, “A Theatre Historian’s Perspective” The Drama Review , Vol. 45, No. 4 (Winter, 2001): 126. 20 number of days it runs on Broadway - to measure its success. With this in mind, using a profit maximization function would not be effective. Instead, Broadway producers aim to maximize sales so they can keep their shows running longer. With many shows closing in the first few weeks if they cannot sell a full house5, it is important that tickets sell for a show to have a chance at running for years and potentially going on to tour. As
Broadway producers form an oligopoly, the model this study uses fits the sale maximization model very well and is supported by current theories.
Additionally, writers and producers must decide what type of show they are trying to produce and how best to support it to increase sales. This is the goal of previews and advertising - to get the word out and increase sales. Brooks McNamara outlines three types of shows, which are reflected in a subsection of three types of musicals. The first is a typical classic musical whose aim is simply to produce a quality work and have a good run. The second is the quirky and artistic work which often makes a political or social statement and can be edgy and even uncomfortable at times for certain audiences. The final type is the mega-musical, or the spectacle. This is a show like Phantom of the Opera whose goal is to generate major hype and cater to the consumer and sell as many tickets and ancillary products as possible.6
As recognition is the goal in all three of these types of shows, either to create a brand of sorts or get a message out, increasing sales is the ultimate objective. Simply increasing ticket prices to increase profits or decreasing them to increase sales will not
5 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 453-460.
6 Brooks McNamara, “A Theatre Historian’s Perspective” The Drama Review , Vol. 45, No. 4 (Winter, 2001): 125-128. 21 solve either profit or sales maximization. Many other factors beyond simply price go into the sale of theatre tickets on Broadway. It is the goal of the second half of this chapter to outline the theory on what these factors are and how they influence the longevity and success of these shows.
Mathematical Model
As producers and everyone involved in putting on a Broadway show are heavily invested in the success of the show, maximizing this success - recorded as the number of days a musical plays on Broadway - is the goal of the artistic team as they put together and plan a show. When maximizing a dependent variable, there are two types of independent variables to take into account. 7 The first of these is the group of variables that producers and directors can control. These will be called the “x” variables. Some examples of these “x” variables are cast size, number of songs in a show and number of previews. The other group comprises truly exogenous variables, or those independent variables that the artistic team and production over which staff have no control. In this study, winning a Tony and whether the musical goes on to be made into a movie are included in these variables, which will be called “z” variables.
So the task given to those who create and put on a show is:
(3.1)
and
7 Hal R. Varian. Intermediate Microeconomics, A Modern Approach Fifth Edition. W.W. Norton & Company, Inc., (1987): 330-335. 22
With representing the prices of goods , representing the prices of goods , as all the quantities of the controllable exogenous variables, as the quantities of the truly exogenous variables and and as the total cost of the corresponding variables.
With this study taking into consideration so many different variables that have the potential to affect the run times of the musicals, the following process will act as if there are only two “x” variables: , and . The total number of truly exogenous “z” variables does not matter, as the producers and directors have no way to influence these variables to try to maximize run time. The maximization problem now reads:
(3.2)
This can also be written as:
(3.3)
Now the first order conditions must be found to solve for the maximizing values of
.
FONC:
1) (3.4)
2) (3.5)
3) (3.6)
The next step if the equations were defined, would be to solve for , which will give the ideal values of the x variables.
After finding values for the controllable variables that maximize the run times, we then want to explain the model that will be used in the regressions in Chapter V. 23
From the theory presented, and the data collected, a production function would fit this model well. In general form this gives us equation 3.7:
(3.7)
In this equation, the dependent variable “y” is the run time of the show.
“β1, β2, …, βn” represent the direct effect categories m1, m2,…, mn have on the dependent variable log(y). In this study, log(y) is used as the dependent variable to fix the problem of non-normal data. This will be discussed in detail in the next chapter.
When the log of both sides is taken, equation 3.7 transforms into:
(3.8)
Each x represents a category of variables that will be discussed in the next section of this chapter. There are nine x variables (m1, m2… m9), each of which is made up of a linear combination of specific variables estimated to have an impact on the dependent variable.
An example of these linear equations would look like a version of equation 3.9:
(3.9)
The next step toward finding the final model is to then plug the linear equation for the m categories (like equation 3.9) back into the linear equation for log(y) (equation 3.8).
From this step, the model now looks like equation 3.10:
(3.10) 24
Now fitting this model to the categories in this study, equation 3.10 becomes:
(3.11)
For the purposes of this study, nine categories are used: Movie ( , Awards
( , Opening Time ( , Theatres ( , Previews ( , Show Length ( , Cast
( , Revivals ( and Notoriety of Artistic Team ( . Within each of these categories is a set of quantitative variables - discussed in Chapter IV - whose effect on the run time of the musicals (y) this study aims to find. To go about finding this, each category variable will be replaced with a linear combination of the numerical variables.
For instance for the category - Cast – two variables, represent the size of the cast. For this specific category, equation 3.11 becomes:
(3.12)
This will distribute to give the equation:
(3.13)
Also shown as:
(3.14)
So if we let and (3.15) (3.16)
By plugging equations 3.15 and 3.16 back into equation 3.13, the model looks like:
(3.17)
Which gives and as the direct linear effect variables and have on the dependent variable log(y) as a combination of the effect they have on their category as well as the direct effect their category has on the variable log(y). This process can be 25 repeated for every combination of variables in each category to find the linear model that will be used to run the regressions in Chapter V.
Influential independent factors
Figure 3.1 outlines the categories of factors that are theorized to have an impact on the longevity of a Broadway show.
FIGURE 3.1
Theoretical Model
Awards Opening Movies Timing
Show Run Time Length Theatres of Broadway Musicals
Revivals Previews
Celebrity Cast Size Status
In this section, the general theories behind why and how these types of factors are influential are discussed. In the next chapter, specific variables used in the collection of data for this study will be discussed in more detail. 26
Dependent variable
Every show that runs on Broadway is hoped to be a success. Different studies have defined “success” in different ways, but for the purposes of this study, the success of a show is recorded in its run time on Broadway from opening to closing night.
Jeffrey S. Siminoff and Lan Ma’s study focuses entirely on how various factors affect the run time of a Broadway show. David Maddison extends this to also consider how various factors affect whether a show receives a prestigious award. Reddy,
Swaminathan and Motley’s study focuses on both run time of a show as well as audience attendance as a measure of success. Theirs was the original study that launched the others. They theorize that audience attendance is a good proxy for profits generated per show, which are not publically available, but also state that the high production costs along with limited seating in theatres leads to run time being an essential dictator of commercial success.8 This study focuses specifically on longevity of the runs of musicals, as all three of the studies use longevity as a dependent variable and discuss the fact that there is a great distinction between musical runs and play runs.
Although they all acknowledge this difference, no one yet has separated the data into musicals versus plays.
8 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 373. 27
Independent Variables
Movie
With the popularity of the film industry being so much higher than the theatre industry, having a movie made of the musical should positively influence the run times, especially if the movie was released before the musical, such as Shrek: the musical. A criticism of the theatre industry is the lack of ability to know what you’re getting yourself into with a show, due to the number of new releases and the lack of trailers and the un-reliability of critics’ reviews.
Awards Won
Every empirical study conducted of Broadway shows finds that winning a Tony
Award positively influences the show’s run time. It is just one way that a show can gain credibility and exposure. Similar to actors being able to advertise their Oscar wins to gain better roles, shows will show off their Tony nominations and wins. However,
Broadway often maintains a balance of styles of shows playing. Sometimes this can influence the run times of shows, based on what other types of shows are already doing well in Broadway theatres. This concept is also reflected in the Tony Awards, with some years having many spectacular musicals nominated for the Best Musical award.
In this case, if they don’t all win, they don’t all close. However, in other years, no spectacular shows were released and so even the winner of a Tony might not go on to have a substantially longer run. On average, however, this category should positively influence a show’s run time.
Opening Timing 28
Theory shows that summer shows should be popular due to tourism as well as more traditional fall opening shows as they fall in the typical theatre season schedule.
Reddy et al.’s study also found a high number of shows which opened in March going on to have longer run times.9 This makes sense as it gives the show enough lead time to build a good reputation before the summer tourist season.
Theatres
There are 40 official Broadway theatres currently in operation in New York.10
Many shows with longer runs will transfer theatres, whether due to sizing limitations, wanting to move to a more well-known theatre, or just having a specific length contract with a given venue. This clearly should positively correlate with run time, as a show with a short run won’t have time to transfer to multiple theatres.
Many producers aim to get into the largest theatres to maximize the potential success for a musical. While a higher seating capacity allows for the potential for higher profits and more hype via word of mouth, thereby extending the run times, it also will cost more and therefore the risk will be higher for producers if the show does not run longer. Producers also tend to try to place shows in theatres closer to Times Square; however, general theory shows that theatre is an experiential good and that people generally make a night out of seeing a show, and therefore they will have pre-planned their evening, reducing the impact of theater location. This variable is expected to be insignificant. Also influencing this is the fact that the farthest theatre is only 2 miles
9 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 370-374. 10 “StageAgent Shows”, StageAgent- Your performing arts connection, 2011, available from http://www.stageagent.com/Shows/All, (Date accessed: April 2011). 29 away.11 Since all of the distances are so clustered, a difference of a couple blocks should not matter. However, as there is so much competition between producers about getting the most “popular” theatres, it would be expected that certain theatres would positively influence the run times of the musicals shown in them.
Previews
Previous studies have found that the number of previews a show had does not affect the success of a show either way. It seems, though, that it should, as a criticism of theatre is the lack of advertising, and previews are a way for the community to see what the show is before it officially opens. Due to the high number of tourists purchasing tickets to shows, local advertisements might not make a substantial difference, leading to the insignificance.
Show Length
Using Moore’s logic on casting, a shorter show would cost less to produce.
Hornby also supports that shorter shows fit the modern day audience’s interests and time constraints better than long, drawn out three act operas.12 However, Theodore and
Loney’s article13, Edney’s journal entry14 and Beggs’ study15 discuss the fact that a more traditional music structure is often successful, which would mean creating a show
11 “Google Maps”, Google, 2011, available from http://maps.google.com/maps, (Date accessed: Spring 2011).
12 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 456. 13 Lee Theodore and Glenn Loney, “Broadway Dancin’” Performing Arts Journal, Vol. 4, No. ½ (May, 1979): 129-141.
14 Kathryn Edney, “Resurrecting the American Musical: Film Noir, Jazz, and the Rhetoric of Tradition in City of Angels” Journal of Popular Culture, Vol. 40, No. 6 (December 2007): 936-952.
15 Anne Beggs, “’For Urinetown is your town…’: The Fringes of Broadway” Theatre Journal, Vol. 62, No. 1 (March 2010): 41-57. 30 with around 15-20 songs, using number of songs as a proxy for length of show. Since this study only looks at musicals, this should be a reasonable way to estimate length of shows.
Size of Cast
There has been much speculation in current studies about how a cast size might affect the success of a show. One theory is that as cast size grows, so do costs, and therefore having a smaller cast allows producers to have a smaller budget and recoup costs more quickly, increasing the probability their show will succeed.
Revivals
Although whether or not a show goes on to have a revival in future years cannot have a direct effect on run time of the original, this study also looks at how many revivals an original musical goes on to have, to see if revivals come from longer running shows or not. The longest running shows (top 14) are a mix of original runs and revivals. Original runs, however, get 12 of these 14 spots and all but 3 of the top 50 shows.16 Revivals have been shown typically to have a shorter run than originals; however, for a show to go on to have a revival, it either has to have been loved the first time it ran, or be expected to do better in the newer market.
16 “Longest Running Broadway Shows (as of May 30, 2010)”, The Broadway League – The Official Website of the Broadway Theatre Industry, 2010, available from http://www.broadwayleague.com/editor_files/Longest%20Running%20Shows_NewLetterhead.pdf, (Date accessed: April 2011). 31
Notoriety of Artistic Team
Certain names are well known among the Broadway writers community. Theory has shown generally that “celebrity status” does not affect actual longevity of the show, can raise costs as “celebrities” are more expensive to hire, and having them in the musical doesn’t change the other facts that have been proven in past studies to be significant. Past studies have shown that having a celebrity cast can improve audience attendance, but in the long run, it does not influence overall run time significantly.17
This chapter has overviewed the general theory that has led to the formation of the model to be tested in this paper. While producers try to maximize profit, they must take many categories of factors into account. The next chapter will define specific variables to be tested within each of these categories.
17 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 370-383. CHAPTER IV
DATA & METHODOLOGY
This chapter describes the data set that was collected to test the model developed in Chapter III. Each variable used in a regression will be discussed, starting with the endogenous dependent variable and then moving on to the exogenous independent variables. Following this section will be a table defining each specific variable and listing its descriptive statistics. Finally, the methodology for econometrically testing the empirical model will be discussed.
Data Set
The data used in this study was compiled from the Internet Broadway Database1 and Broadway Musical Home2. In this study, only musicals were examined. The list of musical names used was found at Broadway Musical Home. Due to the abundance of differences between original runs and revivals, three different sets of data were used.
The first set of data takes into account 178 original musicals that ran on Broadway between 1905 and 2010 and have already closed. The second set takes into account 150 revivals of 76 original musicals that have also already closed. The final set of data
1 “Internet Broadway Database”, IBDB- Internet Broadway Database, 2011, available from http://www.ibdb.com/, (Date accessed: Spring 2011).
2 “Musicals”, Broadway Musical Home, 2011, available from http://broadwaymusicalhome.com/shows.htm, (Date accessed: February 2011).
32
33 combines these two to account for every musical listed at Broadway Musical Home that has opened after 1905 and is not still running.
Figure 4.1 shows the independent variables whose effects are measured on the dependent variable of longevity. The hypothesized effect of each independent variable on run times is located just above the variable. Circular independent variables are continuous and hexagonal variables are discrete. The center square variable is the dependent variable.
FIGURE 4.1
Model used for factors affecting run-times
34
Dependent variables
The dependent variable used for this study is the total number of days a musical ran on Broadway, including its opening night and closing weekend (DAYS). This value was computed by computing the days between the recorded opening night and the date the show closed. For one regression, total performances (performances) not including previews was used as the dependent variable - this value was recorded from the Internet
Broadway Database.3 This was to demonstrate that these variables are interchangeable due to their linear relationship. This study uses days as it is one measure of longevity and it was highly correlated with performances. Additionally, days took into account total time on Broadway as an impact, rather than being influenced by multiple performances of the same musical in the same day or taking a week off.
Since they are so linear - as can be seen in Figure 4.2 - using days as a measure of run time should still match the theory based on performances being the count for longevity. A side by side regression comparison of DAYS_LOG and
PERFORMANCES_LOG can be found in Table 2 in Appendix A. Every variable that theory predicts would be significant is so in both regressions, further proving that both are effective as a measure of success of a Broadway musical.
3 “Internet Broadway Database”, IBDB- Internet Broadway Database, 2011, available from http://www.ibdb.com/, (Date accessed: Spring 2011). 35
FIGURE 4.2
Performances vs. Days
Specials - musicals that ran for a pre-determined length of time shorter than a week - were eliminated from the data set, as nothing factored into their run times, since run time was pre-determined going into the production.
Below in Table 4.1 are the descriptive statistics for the dependent variables including the performance variables.
36
TABLE 4.1
Dependent Variables Descriptive Statistics
Originals n=179 Std
Variable name Description Mean Dev Min Max Total number of days a musical ran on DAYS 820.94 1027.1 0 6548 Broadway- not including previews Log of the total days a show ran on DAYS_LOG Broadway from its opening night 6.0508 1.3249 1.95 8.79 through its last show- includes days off Total number of performances a PERFORMANCES musical had on Broadway- not 926.46 1169.8 0 7485 including previews Log of total number of performances a PERFORMANCES_LOG musical had on Broadway- not 6.217 1.243 2.48 8.92 including previews Revivals n=150 Std
Mean Dev Min Max Total number of days a musical ran on DAYS 285.65 500.24 4 4699 Broadway- not including previews Log of the total days a show ran on DAYS_LOG Broadway from its opening night 4.7937 1.3934 1.39 8.46 through its last show- includes days off Total number of performances a PERFORMANCES musical had on Broadway- not 335.32 612.85 2 5959 including previews Log of total number of performances a PERFORMANCES_LOG musical had on Broadway- not 4.954 1.419 0.69 8.69 including previews Combined n=329 Std
Mean Dev Min Max Total number of days a musical ran on DAYS 576.89 870.35 0 6548 Broadway- not including previews Log of the total days a show ran on DAYS_LOG Broadway from its opening night 5.4759 1.4927 1.39 8.79 through its last show- includes days off Total number of performances a PERFORMANCES musical had on Broadway- not 661.9 1004.1 0 7485 including previews Log of total number of performances a PERFORMANCES_LOG musical had on Broadway- not 5.6465 1.4654 0.69 8.92 including previews
37
Independent Variables
Movie
Whether or not a movie version of the musical was released is accounted for in the variable MOVIE, which takes on a value of 1 if a movie was ever made by the same name and/or on the same storyline. Specifically, MOVIE_BEFORE takes on a value of
1 if the movie version premiered before the musical premiered on Broadway - like Elf and How the Grinch Stole Christmas - and MOVIE_AFTER takes on a value of 1 if the musical opened before the movie premiered - such as Evita and Rent. If a movie was never made of the musical, all three of these variables will take on a value of 0.
Awards
Theory is unanimous behind the thought that winning a Tony will positively influence a show’s success. This study focuses on winning a “Best Musical” Tony as it is the highest honor awarded to a musical each year- similar to a “Best Picture” Oscar awarded to a film. BEST_MUSICAL assumes a value of 1 for the 51 original run musicals that have been awarded it between 1949 and 2008. There are a few years not included because either the musicals are still running, or full data was not available. In
1960, the award was a tie and awarded to both The Sound of Music and Fiorello!.
Opening timing
Theory on opening timing is conflicting. This thesis classifies opening timing into two categories: the year the musical opened, and the month within the year it opened. Generally, the year that the show opened is recorded as “YEAR”. Also the month is recorded numerically (1-12) as “MONTH”. To get more specific results, these 38 variables were further broken into multiple discrete variables. Years were broken into decades: (TENS, TWENTIES, THIRTIES, FORTIES, FIFTIES, SIXTIES,
SEVENTIES, EIGHTIES, NINETIES, THOUSANDS and TWENTY_TENS) ranging from the first year of the decade through the ninth inclusively (ex. 1910-1919). This allowed each decade to have distinct characteristics. The distribution of the musical premiers across decades is shown in Figure 4.3 below. Notice the trends toward more shows after the Fifties and the sharp increase in the most recent decades.
39
FIGURE 4.3
Distribution of shows across Decades
100 90 80 70 60 50 40 30 20 Originals # ofmusical # openings 10 0 Revivals 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 ------Combined - 1919 1929 1939 1949 1959 1969 1979 1989 1999 2009 Originals 1 3 4 11 21 25 17 13 27 48 8 Revivals 0 1 1 7 16 8 24 20 31 41 1 Combined 1 4 5 18 37 33 41 33 58 89 9 Decade
Similarly, the MONTH variable was broken into seasons (SPRING, SUMMER,
AUTUMN and WINTER) with SPRING ranging from February-April, SUMMER
May-July, AUTUMN August-October and WINTER November-January. These seasons are slightly off of the solstice and equinox schedule, which would dictate that SPRING should be March through May and so on; however, the seasons as they are defined for this study are designed to match up with the tourist schedule, with summer vacation taking place in May, June and July, as well as the typical fall theatrical season, which in seasonal companies takes place between August and May.4 The distribution of these season variables can be seen across the three data sets on the next page in Figure 4.4.
4 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 453-454. 40
FIGURE 4.4
Show Openings by Season
140
120
100
80 Original 60
# ofshows # Revivals 40 Combined 20
0 Spring Summer Autumn Winter Seasons
Theatres
There are 40 official Broadway theatres currently in operation in New York.5
This study contains 56 due to the time span stretching back to the 1900’s, thereby including theatres that have since been demolished. Also are included “City Center” and
“New York State Theatre,” which are not technically considered official Broadway theatres, but the shows in this data set that played in them are considered Broadway musicals, so in this specific instance, they are legitimate Broadway theatres.6
Because the theatre in which a musical is presented plays a significant role in the audience members’ experiences, it is important to include many different factors about the theatres in the models. Not only does each musical occupy one of the few Broadway
5 “Internet Broadway Database”, IBDB- Internet Broadway Database, 2011, available from http://www.ibdb.com/, (Date accessed: Spring 2011).
6 Ibid. 41 stages during its run, many transfer to multiple theatres as their runs continue.
TOTAL_THEATRES records the number of different theatres an individual run of a musical plays in. In addition to the total number of theatres, it is also important to include characteristics of the specific theatres. This study takes into account the seating capacity of the theatres in AVG_SEAT which records the average seating capacity of all the theatres a specific show’s run plays in. If a musical played in only one theatre, this theatre’s capacity would be the average.
AVG_DISTANCE is also an average measure between the theatres in which a show plays. This variable takes the average of the distances the theatres are from Times
Square. For the purposes of this study, Times Square is defined as 42nd street between
7th Ave and Broadway (100 feet South East of 7th) in New York City. The distances were calculated as walking distance, as distances were so short and this allowed the trips to go down one way streets.7 With the shortest distance at 0.05 miles and the longest at 1.9 miles, there is not a very large spread in this variable. The locations of the theatres were found on the Internet Broadway Database.8 In the event that there were multiple paths recommended, the shortest distance was recorded. Based on the
Hornby’s theories, producers are constantly striving to play in theatres closest to Times
Square9, so this variable should be negatively correlated and significant.
The final group of variables comprises 56 discrete variables taking on the values of 0 or 1 representing the various theatres in which the musicals played. With so many
7 “Google Maps”, Google, 2011, available from http://maps.google.com/maps, (Date accessed: Spring 2011). 8 “Internet Broadway Database”, IBDB- Internet Broadway Database, 2011, available from http://www.ibdb.com/, (Date accessed: Spring 2011).
9 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 454. 42 theatres and a relatively small set of data, although all 56 variables were computed, only two were eventually used in the model: BROADWAY_THEATRE and
CITY_CENTER as they had the most shows run in them - originals and revivals, respectively. The theory is not decided on the significance of specific theatres so there is no expectation of significance for these discrete variables.
Previews
Previous studies have found that the number of previews a show had does not affect the longevity of a show. Still, as it has been shown to increase audiences10, it is included in the data set. PREVIEWS represents the total number of previews a run of a show had before its official opening night. Revivals also often have previews, so it is included in all three data sets.
Show Length
Theory speculates that longer shows might be less successful than shorter ones, partially due to audience attention span and partially due to extended costs of putting on a longer show.11 The proxy for show length used in this study is the number of songs in the original musical that went on to have an album sold. TOTAL_SONG records the total number of songs in the musical including reprises and finales. It does not include bonus tracks when an album of the show is released. It is also the sum of ACT1, ACT2 and ACT3. ACT1 is the number of songs in the first act of the musical. In the event that
10 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 371.
11 Richard Hornby, “Broadway Economics” The Hudson Review, Vol. 44, No. 3 (Autumn, 1991): 456.
43 a musical has only one act, this will be the same value as TOTAL_SONG. Similarly,
ACT2 is the number of songs in the second act and ACT3 is the number of songs in the third and final act. No musicals were found to have more than three acts, with only eight shows having a third act. For revivals of Broadway shows, the number of songs the original show had was used, as the song list for revivals was often not available because the music was either not recorded or not released.
Size of Cast
Current studies conflict as to the optimal cast size to mount a successful production. This study breaks the cast size of a show into two categories: main or named cast (NAMED) and ensemble cast (ENSEMBLE). NAMED takes into consideration the main cast of a show. Extras, chorus singers, dancers and typically those who have multiple or unnamed roles in the musicals will fall into the
ENSEMBLE category. (Exceptions do exist such as in Avenue Q and Spamalot where the same actor plays multiple main roles). Additionally, the variable CAST_SIZE is the sum of NAMED and ENSEMBLE and accounts for the entire cast size, which involves every paid cast member. As cast sizes were not reported for revivals, they are not included in the revival or combined data sets.
Revivals
As this study makes a distinction in the regressions of the combined data set between original shows and their revivals, the variable REVIVAL_YN is included in the combined data set to differentiate between these two types of shows. In the combined data set, there are 179 first run productions and 150 revivals. Additionally, the variable REVIVALS is included in the original data set as a reflective variable of 44 how many revivals a show goes on to have, which have closed as of the end of 2010.
This variable does not include special shows that ran for just one night or just one week, as they are special arrangements, not a true mounted revival of the show. They also often coincide with a revival already being produced, so including specials would create two entries for the same show.
Notoriety of Artistic Team
In this study, rather than defining who is famous enough to be considered a
“celebrity” in an opening cast, which is not all-inclusive if a celebrity joins the cast midway through the run or if they leave shortly after the show opens, the artistic team of music writers, book writers and lyricists is examined. It is difficult to predict whether having a well known writer of music, book or lyrics will have any effect on the run time. For the original run data set, there are four discrete variables (Stephen_Sondheim,
Richard_Rogers, Andrew_Lloyd_Weber and Oscar_Hammerstein) that assume a value of 1 if their referenced writers worked on creating a show.
Additionally, if the same person wrote the music and lyrics of a show, MU_LY will take on a value of 1; 77 musicals had the same music writer and lyricist. Similarly if a person works on both the lyrics and book, BK_LY will be 1; 51 shows take on this value. If the music and book was written by the same person, MU_BK will be 1- this is only the case for 20 shows. Finally, if any of the previous three variables are equal to one, MU_BK_LY2 will assume the value of 1. There are a total of 120 original musicals on which at least one person worked in at least two of these fields. Having a person write more than just one of these categories could give the show a better sense of cohesion, but also could miss capitalizing on more specialized lyricists, song writers or 45 story writers. There is no telling how this will influence the run times, but it probably will not be significant. Because of this, information on these variables can be found in
Appendix A.
This section concludes with Tables 4.2, 4.3 and 4.4 below which contain a brief description of each variable and its descriptive statistics for each data set. The shaded variables are discrete. Some discrete variables are omitted and can be found in
Appendix A.
TABLE 4.2 Independent Variables Descriptive Statistics- Original Data Set Originals n=178 Variable name Description Mean Std Dev Min Max 1 if a movie of the same MOVIE name or story line was 0.6983 0.460273 0 1 ever released 1 if the musical won "Best Musical" Tony its BEST_MUSICAL 0.2849 0.452641 0 1 premiere season- only first runs are eligible Year specific run of YEAR 1980.3 24.0341 1905 2010 musical premiered Month specific run of MONTH 6.6648 3.586717 1 12 musical premiered 1 if premiered in February, SPRING 0.3799 0.48672 0 1 March or April 1 if premiered in May, SUMMER 0.1508 0.358895 0 1 June or July 1 if premiered in August, AUTUMN 0.1844 0.388863 0 1 September or October 1 if premiered in WINTER November, December or 0.2849 0.452641 0 1 January Average distance of all theatres this run of the AVG_DISTANCE 0.3524 0.23505 0.05 1.55 show played in are from times square
46
TABLE 4.2 Continued
Variable name Description Mean Std Dev Min Max Average # of seats in all AVG_SEAT the theatres this run of the 1415.2 282.2962 597 1935 show played in 1 if the musical played in BROADWAY_THEATRE 0.1006 0.301587 0 1 the Broadway Theatre 1 if the musical played at CITY_CENTER x x x x City Center Total # of theatres this run TOTAL_THEATRES 1.3296 0.724968 1 5 of the musical ran in # of previews a show had PREVIEWS 16.425 15.41627 0 71 this run # of songs in the first act of ACT1 12.436 5.179915 0 45 the musical # of songs in the second ACT2 7.9665 4.59225 0 24 act of the musical # of songs in the third act ACT3 0.257 1.361961 0 11 of the musical Total number of songs in TOTAL_SONGS 20.575 7.363641 0 53 the show CAST_SIZE Size of entire cast 29.95 15.19102 0 67
NAMED Size of main cast 6.4302 5.026825 0 27
ENSEMBLE Size of ensemble cast 23.52 15.80191 0 63
# of revivals an original REVIVALS 0.8492 1.33435 0 6 show went on to have
47
TABLE 4.3 Independent Variables Descriptive Statistics- Revival Data Set Revivals n=150 Std Variable name Description Mean Min Max Dev 1 if a movie of the same MOVIE name or story line was ever 0.94 0.2383 0 1 released 1 if the musical won "Best Musical" Tony its premiere BEST_MUSICAL x x x x season- only first runs are eligible Year specific run of YEAR 1984.1 19.999 1923 2010 musical premiered Month specific run of MONTH 6.5867 3.5106 1 12 musical premiered 1 if premiered in February, SPRING 0.3467 0.4775 0 1 March or April 1 if premiered in May, SUMMER 0.1933 0.3962 0 1 June or July 1 if premiered in August, AUTUMN 0.1933 0.3962 0 1 September or October 1 if premiered in WINTER November, December or 0.2667 0.4437 0 1 January Average distance of all theatres this run of the AVG_DISTANCE 0.478 0.3203 0.05 1.9 show played in are from times square Average # of seats in all AVG_SEAT the theatres this run of the 1626.7 622.78 499 3700 show played in 1 if the musical played in BROADWAY_THEATRE 0.0667 0.2503 0 1 the Broadway Theatre 1 if the musical played at CITY_CENTER 0.1067 0.3097 0 1 City Center Total # of theatres this run TOTAL_THEATRES 1.04 0.2282 0 2 of the musical ran in # of previews a show had PREVIEWS 16.389 14.617 0 79 this run # of songs in the first act of ACT1 10.693 4.6515 0 23 the musical
# of songs in the second act ACT2 7.3267 3.7995 0 18 of the musical 48
TABLE 4.3 Continued
Std Variable name Description Mean Min Max Dev # of songs in the third act ACT3 0.64 1.9739 0 9 of the musical Total number of songs in TOTAL_SONGS 18.66 6.897 0 44 the show CAST_SIZE Size of entire cast x x x x
NAMED Size of main cast x x x x
ENSEMBLE Size of ensemble cast x x x x
# of revivals an original REVIVALS x x x x show went on to have
TABLE 4.4 Independent Variables Descriptive Statistics- Revival Data Set Combined n=338 Variable name Description Mean Std Dev Min Max 1 if a movie of the same MOVIE name or story line was 0.809 0.394073 0 1 ever released 1 if the musical won "Best Musical" Tony its premiere BEST_MUSICAL x x x x season- only first runs are eligible Year specific run of YEAR 1982 22.33244 1905 2010 musical premiered Month specific run of MONTH 6.629 3.547053 1 12 musical premiered 1 if premiered in February, SPRING 0.365 0.482091 0 1 March or April 1 if premiered in May, SUMMER 0.17 0.376392 0 1 June or July 1 if premiered in August, AUTUMN 0.188 0.391667 0 1 September or October
49
TABLE 4.4 Continued
Variable name Description Mean Std Dev Min Max
1 if premiered in WINTER November, December or 0.277 0.447996 0 1 January Average distance of all theatres this run of the AVG_DISTANCE 0.41 0.283867 0.05 1.9 show played in are from times square Average # of seats in all AVG_SEAT the theatres this run of the 1511 478.5269 499 3700 show played in 1 if the musical played in BROADWAY_THEATRE 0.085 0.279465 0 1 the Broadway Theatre 1 if the musical played at CITY_CENTER 0.049 0.215426 0 1 City Center Total # of theatres this run TOTAL_THEATRES 1.198 0.574239 0 5 of the musical ran in # of previews a show had PREVIEWS 16.41 15.04218 0 79 this run # of songs in the first act of ACT1 11.64 5.014515 0 45 the musical # of songs in the second ACT2 7.675 4.254911 0 24 act of the musical # of songs in the third act ACT3 0.432 1.677243 0 11 of the musical Total number of songs in TOTAL_SONGS 19.7 7.207467 0 53 the show CAST_SIZE Size of entire cast x x x x
NAMED Size of main cast x x x x
ENSEMBLE Size of ensemble cast x x x x
# of revivals an original REVIVALS x x x x show went on to have
Econometric Methodology
The above data was regressed using an Ordinary Least Squares (OLS) estimator to find the impact each independent variable had on the number of days a musical 50 would run. Since this study aims to find the direct effect of various factors on the run time of Broadway musicals (DAYS), only an OLS regression was used. As these regressions were run on each data set, it became apparent that the data is non-normal; that is, there is not constant variance across the residuals. This lack of normality of the residuals presents an econometric issue for the regression. The Jarque-Bera (JB) statistic indicates normality problems. If the JB statistic is greater than the critical chi- squared value of 5.99 for the 5% significance level, then the residuals have a non- normal distribution. If the residuals do not have a normal distribution, the t-statistics for the model will not be dependable. Therefore, in order to insure normality, the regressions were then run again, this time using the logarithm of days as the dependent variable.
This transformation still did not correct for the non-normality of the residuals.
The regressions were then run again with robust standard errors which corrected the non-normality of the error term, producing a Jarque-Bera statistic below 5.99 for all regressions used. Removing the outliers in the data set was not an option as the outliers were the shows who achieved much higher than the majority. As that is the goal of
Broadway producers, their removal would make this study far less relevant. Due to the linear transformation of DAYS to log(DAYS), the coefficient produced must be observed as well as the Marginal Effect of each variable calculated.
Another measure necessary to improve estimation was the removal of the variables that were functions of each other - discrete variables such as
MOVIE_BEFORE and MOVIE cannot be run in the same regression. In order to have the most thorough analysis of the data, three regressions were finally run on the original 51 data and two each on the revival data and combined. Various variables were dropped from the model for multiple reasons throughout. These reasons ranged from the variables creating multicolinearity, to theory not supporting their relevance, to the variables not making sense with the data set being used- such as running
REVIVAL_YN on the original data set (all would be equal to 0) or on the revival data set (all would be equal to 1).
This chapter has provided an in depth description of the relevant variables in this empirical study. It also explained the econometric methodology used to analyze the models and the regressions ran. The following chapter will describe the results provided from the regressions and then draw conclusions of what these results explain and if they fit with the theory collected or not and why this might be.
CHAPTER V
RESULTS & CONCLUSIONS
This chapter will analyze the results of the seven Ordinary Least Squares regressions outlined in Chapter IV. The analysis will discuss how each variable used influenced the dependent variable in every regression it was used in. The results will be compared to relevant theory and make sense of why they might have the effect on run time the regressions show they do. The final section of the paper will outline the conclusions that can be drawn from these results and conclude with areas for further exploration on the subject.
Original Run Data
The following table (5.1) presents the results of the three chosen Ordinary Least
Squares (OLS) regressions ran on the data set containing all original productions of musicals listed at Broadway Musical Home.1 The coefficient and t-statistic generated in
Stata are recorded as well as the Marginal Effect of each variable on the dependent variable DAYS. To calculate this, the coefficient is multiplied with the average number of days from the data set. The discrete variables names are shaded and significant t- statistics bolded. This model uses a 5% significance level meaning that the t-statistic must be greater than or equal to |1.96| for the variable to have a significant effect on the runtime. The three regressions that were run on the original data set aimed to capture
1 “Musicals”, Broadway Musical Home, 2011, available from http://broadwaymusicalhome.com/shows.htm, (Date accessed: February 2011).
52
53 which of the 39 independent variables available for this data had a statistically significant effect on DAYS_LOG. If the t-statistic shows the effect is significant, the
Marginal Effect is then used to find the specific effect that variable has- both in direction and magnitude.
TABLE 5.1
Regressions run on the Original Shows data set
Regression 1 Regression 2 Regression 3 n=177 n=177 n=177 Coefficient Marginal Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect (t-stat) Effect
0.778 0.697 572.20 MOVIE_BEFORE 638.69 (2.89) (3.17) 0.683 0.790 648.55 MOVIE_AFTER 560.70 (2.82) (3.35) 0.585 MOVIE 480.25 (2.97) 1.223 1.081 887.44 1.256 BEST_MUSICAL 1004.01 1031.11 (7.38) (5.65) (6.77) 0.2 MU_BK 164.19 (0.59) -0.01 BK_LY -8.21 (-0.05) -0.193 MU_LY -158.44 (-1.05) 0.002 0.002 YEAR 1.64 1.64 (6.64) (10.08) 0.954 783.18 FORTIES (1.97) 0.377 309.50 FIFTIES (0.84) 0.622 510.63 SIXTIES (1.43)
54
TABLE 5.1 Continued
Regression 1 Regression 2 Regression 3 n=177 n=177 n=177 Coefficient Marginal Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect (t-stat) Effect 0.698 573.02 SEVENTIES (1.45) 1.417 1163.28 EIGHTIES (2.68) 1.427 1171.49 NINETIES (2.63) 1.467 1204.32 THOUSANDS (2.78) -0.016 -13.14 TWENTY_TENS (-0.03) 0.251 0.056 45.97 SPRING 206.06 (1.19) (0.28) 0.565 0.101 82.92 SUMMER 463.83 (2.03) (0.39) 0.491 0.392 321.81 AUTUMN 403.08 (2.16) (1.62) 0.153 0.314 257.78 0.261 AVG_DISTANCE 125.60 214.27 (0.44) (0.90) (0.74) -0.000 AVG_SEAT 0.00 (-1.02) 0.535 BROADWAY_THEATRE 439.21 (2.26) 0.190 0.319 261.88 0.269 TOTAL_THEATRES 155.98 220.83 (1.59) (2.54) (2.18) -0.001 -0.025 -20.52 -0.006 PREVIEWS -0.82 -4.93 (-0.08) (-2.72) (-0.85) -0.001 -0.001 ACT1 -0.82 -0.82 (-0.07) (-0.07) 0.046 0.043 ACT2 37.76 35.30 (2.18) (2.40) -0.066 -0.073 ACT3 -54.18 -59.93 (-0.80) (-1.16) 0.016 13.14 TOTAL_SONG (1.41) 55
TABLE 5.1 Continued (2)
Regression 1 Regression 2 Regression 3 n=177 n=177 n=177 Coefficient Marginal Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect (t-stat) Effect 0.040 NAMED 32.84 (1.92) 0.018 ENSEMBLE 14.78 (2.18) 0.024 19.70 0.011 CAST_SIZE 9.03 (3.39) (1.82) -0.013 -0.023 REVIVALS -10.67 -18.88 (-0.15) (-0.29) -0.331 -271.73 -0.464 Stephen_Sondheim -380.92 (-1.15) (-1.55) -0.796 -653.47 -0.634 Richard_Rodgers -520.48 (-1.48) (-1.16) 0.715 586.98 0.769 Andrew_Lloyd_Webber 631.31 (1.52) (1.56) 0.716 587.80 0.262 Oscar_Hammerstein 215.09 (1.17) (0.42)
Revival Data
The next table (5.2) presents the results of the two chosen Ordinary Least
Squares (OLS) regressions that were run on the data set containing all revivals of musicals listed in the Internet Broadway Database.2 Once again, the coefficient and t- statistic generated in Stata are recorded as well as the Marginal Effect of each variable on the dependent variable DAYS. This model again uses a 5% significance level
2 “Internet Broadway Database”, IBDB- Internet Broadway Database, 2011, available from http://www.ibdb.com/, (Date accessed: Spring 2011). 56 meaning that the t-statistic must be greater than or equal to |1.96| for the variable to have a significant effect on the runtime.
TABLE 5.2
Regressions run on the Revival Shows data set
Regression 1 Regression 2 n=141 n=141 Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect
0.504 0.447 MOVIE 143.97 127.68 (1.24) (1.08) -0.310 MU_BK_LY2 -88.55 (-1.60) -0.153 FIFTIES -43.70 (-0.30) -0.444 -0.598 SIXTIES -126.83 -170.82 (-0.99) (-1.05) 1.239 1.073 SEVENTIES 353.92 306.50 (3.14) (2.15) 0.755 0.619 EIGHTIES 215.66 176.82 (1.92) (1.23) 1.462 1.267 NINETIES 417.62 361.91 (3.66) (2.50) 1.440 1.244 THOUSANDS 411.33 355.34 (3.44) (2.41) 1.187 1.199 TWENTY_TENS 339.06 342.49 (1.09) (1.05) -0.068 MONTH -19.42 (-2.68) 0.321 SPRING 91.69 (1.68) 0.378 0.366 AVG_DISTANCE 107.97 104.55 (1.12) (1.08) -0.000 -0.000 AVG_SEAT 0.00 0.00 (-1.96) (-2.36)
57
TABLE 5.2 Continued
Regression 1 Regression 2 n=141 n=141 Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect -0.123 CITY_CENTER -35.13 (-0.29) 1.045 1.091 TOTAL_THEATRES 298.50 311.64 (2.67) (2.73) 0.009 0.008 PREVIEWS 2.57 2.29 (1.09) (0.94) -0.011 -0.017 TOTAL_SONG -3.14 -4.86 (-0.74) (-1.09)
Combined Data
The final results table (5.3) presents the results of the two chosen Ordinary Least
Squares (OLS) regressions that were run on the data set containing all original runs and revivals of musicals used in the last two regressions. The coefficients and t-statistics were generated by Stata and the Marginal Effect of each variable on the dependent variable DAYS was computed in excel. This model also uses a 5% significance level meaning that the t-statistic must be greater than or equal to |1.96| for the variable to have a significant effect on the runtime.
58
TABLE 5.3
Regressions run on the Combined Shows data set
Regression 1 Regression 2 n=327 n=327 Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect
0.847 0.729 MOVIE 488.62 420.55 (3.93) (3.30) 0.014 YEAR 8.08 (4.03) 0.392 EIGHTIES 226.14 (1.48) 0.782 NINETIES 451.13 (3.71) 0.678 THOUSANDS 391.13 (3.72) -0.928 TWENTY_TENS -535.35 (-2.70) 0.050 0.023 AVG_DISTANCE 28.84 13.27 (0.17) (0.08) 0.491 0.575 BROADWAY_THEATRE 283.25 331.71 (2.27) (2.53) 0.574 0.586 TOTAL_THEATRES 331.13 338.06 (4.97) (4.86) 0.007 0.005 TOTAL_SONG 4.04 2.88 (0.72) (0.57) -1.326 -1.347 REVIVAL_YN -764.95 -777.07 (-8.60) (-8.85)
Regression Analysis
Regression results that fit with theory:
MOVIE_BEFORE and MOVIE_AFTER were both found to be significant in both regressions in which they were included, with a marginal effect of over 500 in all cases.
This implies that having a movie made - before or after - will positively affect the run 59 time of the original show, increasing run time by more than 500 days. MOVIE, the sum of these two discrete variables, was also found to have a significant effect on
DAYS_LOG when it was used in the third regression on the original data as well as in the combined data set. This further supports the fact that having a movie made of the musical increases the show’s run time by over a year. This makes intuitive sense, as having a movie before the musical opens increases awareness, and for a movie to be made after a musical opens, the show will have attracted attention enough for film producers to want to invest in it. This matches with the hypotheses shown in Figure 4.1.
MOVIE was not significant when regressed on the revival data set, which could be explained as the hype an original show gets from having a movie made of it is accounted for in the revival data set by there already being an original run of the show.
The BEST_MUSICAL discrete variable reflecting if a musical won the Tony for “Best
Musical” their premier year is extremely significant in increasing run time. This variable is only regressed on the original data set, as a revival is not eligible to win this
Tony award. A musical that wins this award is expected to run at least 887 days longer than a show that did not in all three regressions, all else held equal. This matches with the previously completed empirical studies, which found that winning a Tony will greatly increase the longevity of a show.3
The next set of variables in the regression to analyze are those establishing talent characteristics: MU_BK, BK_LY, MU_LY, Stephen_Sondheim, Richard_Rodgers,
Andrew_Lloyd_Weber and Oscar_Hammerstein. Theory was conflicted as to whether
3 Jeffrey S. Simonoff and Lan Ma, “An Empirical Study of Factors Relating to the Success of Broadway Shows” The Journal of Business, Vol. 76, No. 1 (January 2003): 135-150. 60 or not these variables would help with the success of a show. In all three regressions - original runs as well as the first regression of revival data, however, they all came up as insignificant, which was consistent with Reddy et al.’s empirical study.4 This study finds that including a well-known writer or composer will not increase the run time of the musical.
YEAR has a positive significant effect on the run times of original musicals and on the combined data set, however it is very small, with a mere 1.64 day increase for every year that passes in the original set and 8 days when regressed on combined data. This makes sense, as year is not a consistent variable that trends a certain direction with spikes in premiers in the 60’s and bigger mega-musical shows premiering in the 80’s and 90’s. As Moore predicts in his article, as the years go on, more shows are running for longer amounts of time.5
To help account for these fluctuations, the year variable is broken into decades, which are regressed on all three data sets. While some decades were mentioned in theory as being tied to world events, no study specifically stated which decades should have the longest run times. The decades that were found to be significant did match with the world events and trends on Broadway very well for the most part. FOURTIES - a musical opening between 1940-1949 - was found to have a 783 day longer run than shows that premiered before the 40s. This makes sense, since musicals became the main attraction of Broadway during a time when fewer shows were being produced due to the
4 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 370-383.
5 Thomas Gale Moore, “Broadway Theatre Myths” The Tulane Drama Review, Vol. 10, No. 1 (Autumn, 1965): 95-109. 61
Depression and WWII6, so those already on Broadway stayed. The FIFTIES and
SIXTIES are not found to be significant in any data set. These were the decades just before the larger mega musicals began to form and the economy was just coming out of the Depression and War eras, so there was a lot of turnover in the theatre industry. The
SEVENTIES was found to be significant in both regressions run on the revival data set.
Other studies did not conclude when an ideal time to produce a revival was, but from this observation, the 70’s was a better revival decade than any decade before.
EIGHTIES was found to have a significant positive effect on original shows, but no significant effect on revivals or combined data. NINETIES had a significant positive effect on days in every regression it was used in. This is the second half of the twenty years of mega-musicals aiming to entertain the audiences, so this does make sense.
Being the second decade gives revivals time to catch up and produce larger shows.
THOUSANDS also had a significant positive effect in every regression, likely for a similar reason. TWENTY_TENS was insignificant in the original and revival data regressions, which likely stems from the fact that any show premiering in the time period had a maximum one year run. Also this decade was found to have a significant negative effect on the run time in the combined data set. Theory states nothing to support or argue this, but this makes sense as there is no possibility for a show to have a run time longer than a year that premiered in the 2010’s.
The month premier variables grouping premier months into seasons shows SUMMER as significant in one of the regressions - expected to increase run time by 463 days - and
AUTUMN significant in only one regression - with an expected extension of the run
6 Ibid. 62 time of more than 400 days vs a winter premiere for each of these seasons. This makes intuitive sense because various theory supports extended run times in the summer, autumn and March (which falls into the SPRING variable category,) but many shows that premiere in the winter months are either holiday shows or audience members are siphoned off by other holiday shows. This also supports the fact that MONTH, which ranges 1-12 describing the premiere month, is significantly negatively influencing run times. The higher the month, the closer to December, which is both a WINTER month, and the month that attracts the most themed holiday shows.
BROADWAY_THEATRE is the most popular theatre on Broadway today measuring by the number of musicals it has housed. It was found to be positively significant in every regression it ran in which matches the theory that producers fight to get the
“popular” theatres.
PREVIEWS was found to be insignificant in every regression except one where the marginal effect was only -20. This aligns with Reddy et al.’s study that previews do not have an effect on longevity.
REVIVAL_YN fit the theory perfectly. Every paper referenced in this thesis that mentioned revivals agreed that revivals were not expected to run as long as first runs.
This is very clear in the regressions run on Combined Data, as this variable is very significantly negatively correlated with run time. A revival is expected to run around
775 days - over two years - less than a first run.
Regression results that go against the theory 63
AVG_DISTANCE was found to be very insignificant in all regressions on all data sets.
This goes against the theory that the closer to Times Square a production is, the more successful it will be.7 However, it makes intuitive sense, and matches this study’s hypothesis that attending a Broadway show is an all evening affair, and people do not frequently wander by a theatre and decide to purchase tickets last minute based on what they happen to be closest to.
AVG_SEAT is also found to be insignificant on the original data set; however, it is significantly negatively correlated with run times of revivals. Interestingly though, is the fact that the Marginal Effect is zero in this case. So AVG_SEAT either is insignificant or significantly has no effect on the run times. This makes some sense considering the fact that there are two opposite ways to think about seating capacity. On the one hand, the larger the theatre, the more opportunity there is to make larger profits.
However, there is also a larger risk of failure, as larger theatres cost more to rent and there are higher per/show costs.
The cast size variables were found to be either insignificant or positively significant.
This goes against the theory that larger cast sizes cost more and therefore decrease run time for many musicals.
Regression results theory did not discuss
As BROADWAY_THEATRE was found to significantly extend the run time of its shows in every regression, CITY_CENTER was included as it was the most common
7 Srinivas K. Reddy, Vanitha Swaminathan and Carol M. Motley, “Exploring the Determinants of Broadway Show Success” Journal of Marketing Research, Vol. 35, No. 3 (August 1998): 370-374. 64 heater for revivals. It was found to be insignificant in the regression it ran in so it does not extend run times.
TOTAL_THEATRES was not mentioned in theory, but it makes sense that it would be positive and significant in almost every regression. This is logical, as shows with shorter runs do not have the time to transfer theatres.
TOTAL_SONGS as well as ACT1, and ACT3 were found insignificant in every regression. This could mean that audiences in fact don’t mind longer shows or shorter shows, it’s more about the content. It also could mean they do care about the length and songs were not a great fit as a proxy for show length.
Conclusion
Previous literature and research into the arts industry has left a gap in the area of the
Broadway industry. Only a few studies have attempted to empirically determine the factors that influence the success and longevity of Broadway shows. Not one study has studied musicals independently. With Broadway continuing to play a major role in local and national economies, it is an important process to understand.
With such a high risk involved in producing Broadway shows, it is important to figure out how to make a show successful- that is, how to extend the run time. Other studies have looked at audience attendance as a measure of success. However, even a sold out show cannot recoup its costs if it only runs for a week. It is because of this fact that it is important to empirically determine which factors affect how long a musical will run on Broadway. This thesis aims to help to fill the gap in the academic literature by conducting a current empirical study of how various internal and external characteristics of Broadway musicals affect how long they run on Broadway. As this is only the 4th econometric study, there is much room for further study. 65
APPENDIX A
TABLE 1
Frequency Variable Description Original Revival Combined n=179 n=150 n=329 If a show was a revival or not REVIVAL_YN x x 150 (combined data) If a show premiered between TENS 1 0 1 1910-1919 If a show premiered between TWENTIES 3 1 4 1920-1929 If a show premiered between THIRTIES 4 1 5 1930-1939 If a show premiered between FORTIES 11 7 18 1940-1949 If a show premiered between FIFTIES 21 16 37 1950-1959 If a show premiered between SIXTIES 25 8 33 1960-1969 If a show premiered between SEVENTIES 17 24 41 1970-1979 If a show premiered between EIGHTIES 13 20 33 1980-1989 If a show premiered between NINETIES 27 31 58 1990-1999 If a show premiered between THOUSANDS 48 41 89 2000-2009 TWENTY_TENS If a show premiered after 2010 8 1 9 If Stephen Sondheim worked on Stephen_Sondheim 14 x the musical If Richard Rodgers worked on the Richard_Rodgers 10 x musical If Andrew Lloyd Webber worked Andrew_Lloyd_Webber 5 x on the musical If Oscar Hammerstein II worked Oscar_Hammerstein 8 x on the musical 1 if Music and Book written by MU_BK 20 12 32 the same person 1 if Book and Lyrics written by the BK_LY 51 55 106 same person
66
Frequency Variable Description Original Revival Combined n=179 n=150 n=329 1 if Music and Lyrics written by MU_LY 77 45 122 the same person 1 if Music, Book or Lyrics written MU_BK_LY2 120 94 214 by the same person 1 if a movie of the same name or MOVIE_BEFORE story line was released before the 45 x x musical premiered 1 if a movie of the same name or MOVIE_AFTER story line was released after the 79 x x musical premiered
TABLE 2
Original Data: Regression 1 Regression 2 days_log performance_log n=177 n=175 Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect
0.778 0.622 movie_before 638.6945 576.2569 (2.89) (2.47) 0.683 0.615 movie_after 560.7048 569.7717 (2.82) (2.61) 1.223 1.210 best_musical 1004.015 1121.014 (7.38) (7.53) 0.2 0.129 mu_bk 164.1888 119.5131 (0.59) (0.40) -0.01 -0.044 bk_ly -8.20944 -40.7642 (-0.05) (-0.23) -0.193 -0.195 mu_ly -158.442 -180.659 (-1.05) (-1.14) 0.002 0.002 year 1.641888 1.852916 (6.64) (6.69) 0.251 0.185 spring 206.057 171.3947 (1.19) (0.95) 0.565 0.492 summer 463.8334 455.8174 (2.03) (1.84) autumn 0.491 403.0836 0.353 327.0397 67
(2.16) (1.64) 0.153 0.029 avg_distance 125.6044 26.86728 (0.44) (0.08)
Original Data: Regression 1 Regression 2 days_log performance_log n=177 n=175 Coefficient Marginal Coefficient Marginal Variable name (t-stat) Effect (t-stat) Effect -0.000 -0.000 avg_seat 0 0 (-1.02) (-0.42) 0.535 0.492 broadway_theatre 439.2051 455.8174 (2.26) (2.10) 0.190 0.182 total_theatres 155.9794 168.6154 (1.59) (1.38) -0.001 0.005 previews -0.82094 4.632291 (-0.08) (0.58) -0.001 -0.008 act1 -0.82094 -7.41166 (-0.07) (-0.47) 0.046 0.033 act2 37.76343 30.57312 (2.18) (1.70) -0.066 -0.027 act3 -54.1823 -25.0144 (-0.80) (-0.46) 0.040 0.038 named 32.83776 35.20541 (1.92) (1.86) 0.018 0.018 ensemble 14.77699 16.67625 (2.18) (2.26) -0.013 -0.008 revivals -10.6723 -7.41166 (-0.15) (-0.09)
68
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