AN ECONOMIC INVESTIGATION INTO the CORRELATION BETWEEN STAR POWER and BOX OFFICE REVENUES in HOLLYWOOD a THESIS Presented To

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AN ECONOMIC INVESTIGATION INTO THE CORRELATION BETWEEN STAR POWER AND BOX OFFICE REVENUES IN HOLLYWOOD

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

Katie Wirth

March 2016

AN ECONOMIC INVESTIGATION INTO THE CORRELATION BETWEEN STAR POWER AND BOX OFFICE REVENUES IN HOLLYWOOD

Katie Wirth

March 2016

Mathematical Economics

Abstract

This paper examines the relationship between box office revenues and star power through two innovative measures of celebrity influence. By observing the number of visits to a star’s IMDB profile page, the STARmeter ranking system provides a continuous measure of celebrity popularity. In addition, by using E! Online to calculate the number of positive and negative article appearances of each celebrity, we obtain a measure of public image. This process is used for the top two celebrities of each film. The results suggest casting celebrities with a positive public image will in fact increase box office revenues. Furthermore they suggest that having a second superstar in a film may be the key to box office success.

KEYWORDS: (box office revenue, star power, media, movie industry, movie stars) JEL CODES: (L82, L25, M31)

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

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TABLE OF CONTENTS

ABSTRACT ii

1 INTRODUCTION ...... 1

2 LITERATURE REVIEW ...... 5 2.1 Star Power ...... 5 2.1.1 Theorizing Star Power ...... 6 2.1.2 Measuring Star Power ...... 7

3 THEORY ...... 10 3.1 Preliminary Model ...... 10 3.2 Accounting for Star Power ...... 11 3.3 Empirical Model ...... 12

4 DATA AND METHODS ...... 14 4.1 Dataset ...... 14 4.2 Dependent Variable ...... 16 4.3 Independent Variables ...... 16 4.3.1 Financial Variables ...... 16 4.3.2 Classification Variables ...... 17 4.3.3 Star Power Variables ...... 18

5 RESULTS ...... 19 5.1 Model 1 ...... 19 5.2 Model 2 ...... 20 5.3 Model 3 ...... 21

6 CONCLUSION ...... 23

7 APPENDIX A ...... 27 APPENDIX B ...... 28 APPENDIX C ...... 29

APPENDIX D ...... 30 APPENDIX E ...... 32

REFERENCES ...... 33

Introduction

One of the largest and most profitable industries in the United States is the film industry. In the year 2015 alone 9,513 movies were released, 1,340,883,711 tickets were sold and the total domestic box office revenue was $11,303,653,087 according to the

Internet Movie Database (2016). With the massive amount of movies made each year, the consumer faces a difficult decision each time they enter a movie theater. While each individual undoubtedly has unique preferences in their movie selection, the overall market trends of movie selection can be modeled. Hollywood has major financial incentives to describe this pattern of movie selection, as their profitability depends on their ability to draw in viewers. Thus much research has been done in determining what makes a box office hit. Most of the research agrees that the production budget of a film is the biggest predictor of financial success, while other less significant variables such as the genre, the MPAA rating, viewer ratings, critic ratings, the time of year released, whether or not the movie is a franchises and celebrity star power are often disputed.

Hollywood is well known for its excess of high budget films with special effects and high-paid actors. In 2007, the average production budget of a major studio film was

$106 million according to the Motion Picture Association of America (Roos, 2009). Of this budget on average $35.9 million went to marketing with the goal of drawing in massive crowds on opening weekend. This marketing strategy is important, as the fate of most Hollywood films are largely determined on opening weekend. The rest of the budget is spent in various ways including rights to the script, production staff salaries, set

1 construction, wardrobes, craft services, special effects, actor salaries and any other expenses incurred. Unsurprisingly in the past 20 years, the industry has seen some of the biggest special effects budgets ever, with Spider-Man 3 at $258 million, Harry Potter and the Half Blood Prince at $250 million and Superman Returns at $232 million (Roos,

2009). These extreme budgets have transformed industry standards and in turn viewer expectations of major blockbusters.

The other major expense in the production budget is actor salaries. Given the enormous amount of uncertainty and high risk surrounding the movie making process, familiar actors are often cast in leading roles in order to draw in a pre-existing fan base.

In studio films, the traditional safe bet is to spend about $20 million on one well-known actor. While $20 million is the average cost for a big name celebrity, this number can range from Leonardo DiCaprio at $25 million to Seth Rogen at $8.4 million (Galloway,

2015). This decision is logical as movies are an experience good and the audience uses the popularity of the stars to decide whether or not to purchase a ticket. Although this strategy seems straightforward, it is often difficult to determine which celebrities will add enough value to a film for the payout to be worthwhile.

It is evident that the presence of a celebrity in a film is important to Hollywood studios and should be included when modeling box office performance. Many studies have attempted to model this relationship, however, it is difficult to quantify celebrity influence. The first problem is determining whether a celebrity should be valued based on talent or popularity. Measuring talent implies celebrities are cast based on their acting abilities and measuring popularity implies celebrities are cast based on their marketability. Therefore, these two measures often lead to conflicting results. For

2 example, Leonardo DiCaprio who makes on average $25 million a film and was the tenth highest grossing star in 2015 has never won an Academy Award. Using a measure of talent would suggest he has little star power, but using a measure of popularity or earnings would suggest he has a great deal of star power. Both measures are valid to some extent and have been modeled in past studies; however, this model will assume celebrities are valued based on their popularity. In particular, this study will use two different measures of star power: the popularity amongst moviegoers and the celebrity’s public image.

It is important to consider which celebrities the viewers are most interested in, as they are the target audience. In order to account for this, this study will include a measure of star power based on the most searched celebrities online. This measure ranks 2.3 million celebrities based on the number of profile views in a week, providing a weekly ranking of popularity amongst viewers. By measuring popularity amongst viewers the study should provide more accurate results.

It is also important to include a measure that accounts for the public’s attitude towards celebrities. Inspiration for this measure comes from many popular tabloids such as People, Intouch and US, who devote their pages to stories about celebrities. Celebrity journalism simultaneously reflects the popularity of a given celebrity and promotes their popularity. Consequently, the celebrities try to generate more stories to remain in the tabloids and in turn the public eye. Another important aspect is the manner in which a celebrity is portrayed. Certain celebrities constantly receive negative tabloid exposure and thus gain an unfavorable reputation. Some studies have used the number of tabloid appearances as a measure of popularity; however, none so far have considered the overall

3 tone of the articles. By categorizing the articles as negative or positive, this study aims to measure the public image or reputation of a celebrity.

The following chapters outline a new model to capture the relationship between star power and box office success. By using two measures of celebrity popularity, this model will provide more in depth information about the relationships than past studies.

4 Literature Review

This section provides a brief overview of theories as to why star power is an important predictor of box office success. It also discusses the best ways to measure the intangible effects of star power.

Star Power

The added value of a star has long been the subject of debate within both

Hollywood and in the economics literature. Overwhelmingly, studies have found a positive correlation between star power and financial success. Studies by Faulkner and

Anderson (1987), Wallace, Seigerman and Holbrook (1993), Prag and Casavant (1994),

Sochay (1994), Sawhney and Eliashberg (1996), Albert (1998), Neelamegham and

Chintagunta (1999), Basuroy, Chatterjee and Ravid (2003), and Ainslie, Dreze and

Zufryden (2005) all confirm this relationship.

Several studies however have obtained opposing results. De Vany and Walls

(1999) finds star power to be insignificant and furthermore finds the production budget of a film to be the biggest predictor of a film’s success. In another study, Bart (2007) suggests a new trend in Hollywood is stars actually reducing box office success. As evidence he cites stars who have all headed recent box office flops such as: Ben Stiller,

Jodie Foster, George Clooney, Halle Berry, Brad Pitt, Mark Wahlberg, Joaquin Phoenix,

Jude Law, and Jamie Foxx. Further research is necessary to determine whether this emerging trend is the rule or the exception.

5 Theorizing star power. As most of the economic literature agrees that star power is a significant predictor for financial success, there are many theories to explain the phenomenon. Ravid (1999) offers two theories as to why a star will increase the economic value of the film. The first theory is that stars capture most of their value added. For example, until the 1950s, Hollywood stars would sign studio contracts. If the stars were successful, the studio would increase its market value. Therefore, each unique star captures their expected added value. This theory can be supported by an observed increase in an actor’s pay after a successful film. Weinraub (1995) reports Alicia

Silverstone increasing her fee to $5 million after the success of Clueless where she received only $250,000. Ravid’s second theory proposes the inclusion of a star is based on the star’s pre-existing knowledge of a quality script and production plan. Breese

(1992) suggests the process of gradual attachment of talent to a movie – where stars wait to sign on to a movie until it is in production, supports this theory. Through a signaling model Ravid (1999) finds circumstantial evidence for each theory, however, star power is ultimately overshadowed by the film’s production budget.

Suárez-Vázquez (2011) offers three more hypotheses as to why star power may predict success for a film. The first hypothesis is that the presence of a star in a film will increase spectator expectations. Likewise, the “Disney strategy” articulated by Dekom

(1992) suggests that once a person is a star, he or she will be recognized and valued by the public. Disney exploits this public recognition to make large profits. The second hypothesis is that having a star is in a film will increase the likelihood of a viewer recommending the film. Deuchert, Adjamah and Florian (2005) propose that the perceived utility one receives from a film is directly correlated to the information

6 available about the cast. Similarly, the more aware the public is about a particular star, the more likely they are to engage in conversation about the star’s new film. The third hypothesis is that having a star in the cast of a film cushions the effects of negative criticism. Indeed, Basuroy (2003) finds evidence that negative criticism is less costly to a film when it has a star in its cast.

Measuring star power. The idea of star power is somewhat abstract and difficult to measure, thus a large portion of the literature focuses on the potential measures for its abstractness. Some popular measures of star power are: Academy Awards nominations/wins, Golden Globe nominations/wins and the lifetime earnings of a star.

Multiple studies have used award nominations as a proxy measure for star power.

A study by Litman (1983) found a significant relationship between Academy Award nominations or winnings on revenues. Litman and Kohl (1989) also found that the involvement of top directors and stars to be significant predictors of revenue. While several studies have obtained significant results using award nominations, this method has many limitations. According to Nelson and Glotfelty (2012) these include: the limited sample of nominations and awards for each year, and the frequent scenario where an actor/actress has box office success, but is not nominated for an award or vice versa.

These limitations make using award nominations a restricted measure of star power.

The lifetime earnings of a star are also frequently used as a measure of star power.

This measure provides the monetary value of a star, which may be useful when looking at box office revenues. However, Nelson and Glotfelty (2012) argue that many franchises have financial success employing actors who would not be traditionally considered stars, but happen to have box office success. For example, during 1999-2005, Samuel L.

7 Jackson, Christopher Lee, Hugo Weaving, Orlando Bloom, Ewan McGregor, Ian

McKellen, and Elijah Wood were all in the top ten earners in Hollywood. Each actor appeared in either the Star Wars sequel trilogy or The Lord of the Rings trilogy.

Traditionally they would not be considered stars, however, using the measure of lifetime earnings they would be top stars.

Clearly there are flaws to each popular method of measuring star power. To remedy this problem, in recent years more unconventional measures of star power have been used. For example, Nelson and Glotfelty (2012) use STARmeter rankings from the

Internet Movie Database or IMDB in order to account for a much broader range of actors and actresses. This system also ranks the celebrities relative to each other, which may provide more precise results than previous measures. Finally, the rankings are generated based on the number of times someone views the actor’s profile, making the measure consumer based. To further expand this method, Nelsen and Glotfelty include a variable measuring the combined star power of the top three actors in a film, which accounts for the potential synergy from employing multiple actors with star power. The results suggest a significant amount of added value from the interaction of multiple stars in a film. The theory as Bing (2002) explains, “is what one studio exec calls ‘the one plus one equals three’ model, in which two superstars are cast in the hopes that their joint cachet will pay off”. Using this method, Nelson and Glotfelty found significant results supporting the importance of star power on financial success.

In another study, Treme and Craig (2013) use data from People Magazine to analyze the effect of gender and age on box office success. By tracking the number of article appearances for each celebrity they create a new measure of star power based on

8 media exposure. This measure considers the public image of a celebrity rather than their level of talent. Using this method, Treme and Craig found that female celebrity exposure decreases box office success, whereas male celebrity exposure increases box office success. A secondary finding from the study is that casting a male lead over 42 years old decreases box office revenues. These results are much more specific than previous findings, leading one to believe that this measure of star power is more accurate than previous measures.

Clearly there are advantages and disadvantages to each measure. In order to illustrate these, Table 2.1 in Appendix A compares several of the aforementioned measures of star power for twenty of the top grossing stars in 2015.

9 Theory

After reviewing the relevant literature, there seems to be a general consensus that star power is in fact a significant predictor of box office success, however, there is still disagreement about how this should be incorporated into a model. The purpose of this study is to analyze the relationship between celebrity star power and the financial success of a film. In order to accomplish this, the model incorporates a consumer driven measure of star power. In addition, it uses a secondary measure of star power that accounts for the public image of a celebrity to analyze the importance of a positive reputation. Ideally, the inclusion of these two measures will provide enhanced results about the particular relationship and further support previous findings.

Preliminary Model

For this study, numerous predictors of box office success were considered based on the reviewed literature. Undisputedly, the most significant variables were financial measures such as: production budget, the maximum number of theaters a movie was screened at, the opening weekend revenues, international revenues, and the number of theaters screening the movie on opening weekend. These variables account for the inherent value of a film to the production company, thus signifying producer expectations of box office success. There is also a natural correlation between these variables and star power, as films with higher budgets tend to cast more expensive stars. As some of the financial measures are redundant, the model only includes the production budget, the

10 maximum number of theaters and opening weekend revenues, or Budget, Screens and

OpeningWeekend.

Another important category of predictors is classification variables. Certain films appeal to certain audiences, implying categories such as the genre, whether or not the film is a sequel and MPAA ratings are important to box office success. These variables measure consumer preferences and trends in films. The model incorporates the variables

Action, Adventure, Comedy, Horror, Thriller, PG, PG13, R and Sequel to account for these consumer preferences. As an additional measure for consumer preferences, the viewer ratings are included as the variable Rating. In the literature this variable is highly disputed and measured in numerous ways. While critic reviews are occasionally used, this study will use the ratings of actual moviegoers to measure their preferences.

Accounting for Star Power

To overcome shortcomings of previous studies of this sort, modifications to the way star power is measured are necessary. The first modification is to use a more consumer based measure of star power to increase the relevance of the measure. By using proxy variables such as award nominations, little insight is gained into the popularity of the celebrity according to the consumer. Following the study by Nelson and Glotfelty

(2012), IMDB STARmeter rankings will be used to measure star power. There are several advantages to using this system. First, the system explicitly reflects consumer preferences in an actor or actress rather than ranking artistic abilities. Second, the system does not ignore new and upcoming actors who may not appear in large-scale films.

Finally, the rankings allow for 2.3 million actors and actresses to be studied rather than the handful that is nominated for awards. In addition to using the rankings of the top two

11 billed stars in each film, the model will include a measure of the combined rankings of the top three stars. This third measure captures Nelson’s “ensemble effect,” where a film casts multiple stars in hopes of dramatically increasing earnings. Finally, to allow for the possibility of diminishing returns to star power, the squares of each of these variables will be included. In the model, these variables appear as Star1, Star2, Startot, Star12, Star22 and Startot2.

The second modification to the model is the inclusion of a measure to capture a celebrity’s public image. A previous study by Treme and Craig (2013) counted the number of times a celebrity appeared in People Magazine over a given time period. This measure provides insight into the popularity of the celebrity from the perspective of the media. An advantage to this measure is the widespread exposure of tabloids, which has been increasing over the past decade. In addition, the measure focuses on the impact of celebrity instead of the level of talent, allowing for a more refined gauge of star power.

While this measure is innovative, further categorizing the magazine appearances may enhance the results. By classifying article appearances as positive, negative or neutral, a measure of public image is yielded. This allows the model to capture the effects of a positive or negative reputation on box office success. The variables are represented as

Npub1, Npub2, Ppub1, Ppub2, Tpub1 and Tpub2 in the model.

Empirical Model

The resulting empirical model is as follows:

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12 Using the Ordinary Least Squares (OLS) method, regression analysis should provide valuable insight into the relationships theorized in this section. By using several financial and classification variables along with improved measures of star power, this model provides a new perspective into box office success.

13 Data and Methods

This section discusses the sources of data and their purposes in the empirical model. Data in this study are primarily drawn from the multiple factions of the Internet

Movie Database including IMDBpro and Box Office Mojo. Additional data were drawn from the celebrity gossip site E! Online.

Dataset

The selected variables for the empirical model are based on the previously recognized predictors of a box office hit, as evaluated in the literature review. The dataset acquired from Box Office Mojo contains financial information on top 100 highest domestically grossing films of 2015. While the dataset included ten different financial measures, only four measures were used in this study, including: the film’s domestic revenue, the opening weekend revenue, the maximum number of theaters the film was screened at and the film’s production budget. The year 2015 was selected in order to minimize the number of franchised movies, a trend that was largely successful in the

2000s, but may skew the data. Data on dummy variables such as genre and Motion

Picture Association of America (MPAA) rating were then taken from IMDBpro.

The data from celebrity star power comes from the STARmeter rankings on

IMDBpro. The STARmeter rankings are obtained by calculating the number of views an actor or actress receives on their IMDB profile page. The system ranks over 2.3 million actors, actresses and directors such that 1 represents the highest level of star power. A star’s ranking is measured and recorded weekly so that at any given point in time their

14 ranking may be obtained. For each film, star power of the top three listed members of the cast was measured a year prior to the release of their film. This accounts for the celebrity’s star power well before the release of the film and negates any increases in star power due to publicity from the film’s release. This gives 300 total celebrity rankings in the dataset.

The data for a secondary measure of celebrity star power – the star’s public image, were obtained from E! Online. This website was selected due to its prominence amongst celebrity gossip websites. It also includes fair amounts of positive and negative articles, which limits bias that is prevalent on other sites. Data were collected on the number of article appearances of the top two celebrities in each film, however, in order to exclude film specific promotional publicity, any articles released three months prior to the film’s release were excluded. Therefore the number of articles in the 12 months prior to the promotional period was recorded. The data was then further broken down into three categories: positive publicity, negative publicity and neutral publicity. Positive publicity includes volunteer work, social activism, charity work, marriages, childbirth and other personal successes. Negative publicity includes breaking the law, illegal substance abuse, rude comments, extramarital affairs, breakups and other negatives acts.

Neutral publicity is any other mention in an article where the star is portrayed in neither a positive or negative light. Therefore, the dataset contains data on the number of article appearances of 200 celebrities. While some celebrity’s appear more than once, the time period from each film provides distinctive data. Table 4.1 in Appendix B presents data on a sample of twenty actors/actresses from the dataset. Both STARmeter rankings and breakdowns of positive and negative article appearances are included.

15 Dependent Variable

The dependent variable in this study is the domestic box office revenue of a given film in 2015 dollars. By using only domestic revenues any factors associated with foreign markets are eliminated from the model. BoxOffice is the dependent variable in the model.

Box office revenues will be a function of a number of film specific factors.

Independent Variables

Financial variables. This model incorporates several film specific financial measures in order to gain insight into the film’s production scheme. These variables may have joint significance, as they are largely dependent on the production budget of a film.

Budget is the measure of a film’s total production budget. The higher the budget of a film, the more money a film has for special effects or elaborate sets. A film with a higher production budget can also hire more expensive celebrities and therefore increase star power. Thus the coefficient on production budget is expected to be positive. Screens is the maximum number of movie theaters that a film was ever screened at. As movie theater screens are scare resources, they are allocated to films that will bring in the greatest revenue. This suggests the coefficient on the number of screens should be positive. There is, however, a natural correlation between Budget, Screens and star power, as the higher cost of stars inflates the production budget and therefore increases the number of theaters willing to screen the film. Prag and Casavant (1994) believe this positive correlation may make it difficult to interpret the results, as the coefficient on star power may be underestimated. One remedy for this problem that Nelson and Glotfelty

(2012) had success with, is to run the regression without Budget and Screens.

OpeningWeekend is the total revenue from the opening weekend of a film. High opening

16 weekend revenues may signal popularity amongst consumers. It also may lead to higher revenues due to the “word of mouth” theory – that the more viewers that have seen a movie will increase the chances of it being recommended such as Suárez-Vázquez (2011) hypothesizes. Hence a positive relationship is expected between opening weekend revenues and total box office revenues.

Classification variables. The model uses several different dummy variables in order to classify each film and attempt to identify consumer preferences. Action, Adventure,

Comedy, Horror and Thriller are dummy variables that represent the genre of a film, where dramas were omitted to eliminate multicolineraity. The genre of the film may draw in different audiences, however it is difficult to predict which genres are popular and will have a positive impact. PG, PG13 and R are dummy variables that account for the

MPAA rating of a film, where movies rated R were omitted to eliminate multicolinearity.

According to the reviewed literature, films with a larger audience should have greater revenues, and therefore, PG should be positive while PG13 and R should be negative.

Sequel is a dummy variable that designates whether or not a movie is a sequel. This excludes the first movie of a franchise in order to account for viewer expectations based on previous experiences. Holson (2003) and McNary (2007) suggest movies that are a part of a franchise have a built in audience leading to greater box office appeal.

Accordingly, the coefficient for Sequel is expected to be positive. Rating is the average viewer rating of a film, taken from IMDBpro. While the most popular measure of ratings used in studies are critic’s reviews, this measure can be inaccurate as critics typically have different standards for movies than consumers have. By using the IMDB ratings, we obtain a reflection of the taste of actual moviegoers. IMDB ratings are measured on a

17 scale from 1-10, where 10 is the highest and 1 is the lowest. Thus we expect a positive relationship between ratings and total revenue.

Star power variables. This study uses two different measures of celebrity influence in order to evaluate the impact of star power on box office revenues. Star1, Star2 and

Startot are the IMDB STARmeter rankings. For each film, Star1 represents the

STARmeter ranking of the top billed star, Star2 is the ranking of the second star and

Startot is the combined star power of the top three stars. By implementing a measure of the top three’s combined star power, we can observe the effect of casting multiple stars.

Since 1 is the highest level of star power, we expect an inverse relationship between star power and box office revenue and so the coefficients on Star1, Star2 and Startot are expected to be negative. Star12, Star22 and Startot2 account for the possibility of diminishing returns to star power. This measure is the Star variable squared. Due to the previously mentioned inverse relationship of the STARmeter ranking system, we expect these coefficients to be positive. Npub1, Ppub1, Tpub1, Npub2, Ppub2 and Tpub2 are measures of the top two star’s public image based on data from E! Online. For each film,

Npub represents the number of negative articles written about the star, Ppub represents the number of positive articles written about the star and Tpub represents the total number of articles written about the star, which includes neutral articles. These variables capture the reputation of a celebrity and test the impact of a positive or negative public image. It is likely that a positive public image leads to box office success and so the sign will be positive. However, it is unclear if a negative public image will decrease box office success – or if the mere increase in publicity will increase revenues. Summary statistics for all 26 variables are reported in Table 4.2 in Appendix C.

18 Results

The purpose of this chapter is to display the results of the Ordinary Least Squares regression analysis of the empirical model. Three specifications will be discussed in order to uncover the complex relationship between box office revenues and celebrity star power. Table 5.1 in Appendix D displays the results of each model.

Initial diagnostic testing of the full model suggested a need for corrective measures. While the model passed the White Test for heteroskedasticty, it did not pass the Jarque-Bera Test for normality. To correct for this, the dependent variable BoxOffice was transformed with logarithms. However, after corrective measures the model still failed to pass the Jarque-Bera Test. Although normality is a fundamental assumption of the OLS model, the non-normal distribution is not a problem because as the sample size increases, the distribution of the error term approaches a normal distribution. This implies that the results will still be asymptotically valid.

Model 1

The initial specification includes all of the variables from the empirical model; however, several of the variables were transformed. Along with BoxOffice, the variables

Budget, Screens and OpeningWeekend were transformed with logarithms in order to be interpreted as percentages. This model explains approximately 68% of the variance in domestic box office revenues. In addition 18 of the 25 predictors are statistically significant at the 10% level. The variables LogBudget, LogScreens and

LogOpeningWeekend are all significant at the 5% or better and the coefficients have the

19 correct signs. However, only four of the ten classification variables are significant at the

5% level. The signs of the coefficients of these variables are all plausible except for

Sequel, which was expected to be positive. In addiction, only two of the 12 star power variables are significant at the 5% level. The signs on Star2, Startot, Star12, Star22 and

Startot2 are all correct, however, the sign on Star1 should be negative. All of the variables measuring publicity are positive, which may be expected, however, they are not very statistically significant.

Although this model explains a large amount of variance among box office revenues, and a majority of the variables are significant, the model does not explain much of the influence of star power. Further examination of the variables Star1, Star2, Startot,

Star12, Star22 and Startot2 show the data are not normally distributed. Skewness and kurtosis tests along with normality plots confirm the non-normal distribution. As normality is a fundamental assumption of the OLS model, corrective measures are necessary in order for the estimators to be unbiased. To remedy this, the data for the variables Star1, Star2 and Startot were transformed using logarithms. Unfortunately this transformation cannot be performed on the variables Star12, Star22 and Startot2 without leading to multicolinearity and so the variables are dropped in the subsequent models.

After being transformed, the star power variables display a much more normal distribution and pass the skewness and kurtosis tests. Figure 5.1 in Appendix E presents normality plots of the variables before and after being transformed by logarithms.

Model 2

This specification includes all of the original financial and classification variables along with the transformed star power variables. The model explains approximately 76%

20 of the variance in domestic box office revenues and 17 of the 22 estimators are significant at the 10% level. All three financial measures are significant at the 1% level and display the correct sign. Only four of the ten classification variables are significant at the 5% level and the variables Adventure, Horror, PG, R and Sequel all changed signs from the prior specification. Out of these changes, PG, R and Sequel now reflect the correct expected sign. The transformed star power variables LogStar1 and LogStar2 are both significant at the 5% level and LogStartot is significant at the 10% level.

Additionally, the variables all exhibit the correct sign. While the variables measuring negative publicity are both insignificant, the variables measuring positive and total publicity are all significant at the 10% level or better and have positive signs.

While this specification provides adequate results, the financial variables appear to dominate the model as hypothesized in the reviewed literature. This problem comes from the positive correlation between the financial measures and star power, which may cause the effects of star power to be underestimated. An analysis of the data indeed shows high correlations between the production budget and the star power variables, as eight of the twelve variables have a correlation above .5. The number of screens is also highly correlated with seven of the twelve star power variables. To correct for this, a new model will be estimated without the variables Budget and Screens.

Model 3

The third specification of the model excludes the variables Budget and Screens and uses the transformed star power variables. The model explains approximately 63% of the variance in domestic box office revenues and 16 of the 20 estimators are significant at the 10% level. The variable LogOpeningWeekend is now only significant at the 5% level,

21 but the coefficient increased from .279 to .358. In addition all of the classification variables except PG are significant at the 10% level and variables Adventure and Thriller changed signs. While only five of the nine star power variables are significant at the 5% level, all but LogStartot and Tpub2 increased in magnitude. As in the previous models the signs of the STARmeter variables are all negative and the sign of the publicity variables are all positive. Although this model more accurately captures the effect of star power, the Ramsey Reset Test suggests there is omitted variable bias from dropping the two variables. This is expected due to the large influence of the financial variables.

Overall, the significance of star power can be observed in each model.

Throughout all three models, when the coefficients on star power are significant, they support the known relationship with box office revenue. In addition to the correct signs, the coefficients in the first two models appear similar in magnitude and increase slightly in the third model. While star power does appear to be important, the financial variables are still the most influential and significant predictors of each model.

22 Conclusion

This study has addressed several flaws in previous studies on the impact of celebrity on box office success. By using two separate measures of star power, the model more effectively exposes the benefits of casting big name celebrities. This modification is essential in developing the relationship between celebrity and box office revenues, which may help maximize future profits.

Results from the data analysis support numerous findings in the relevant literature. First, the most significant and influential predictor of box office success was in fact, the production budget. This is unsurprising as films with larger budgets can afford better special effects, sets, celebrities and marketing than smaller films can. Similarly, the maximum number of theaters a film is screened at was a large predictor of economic success. The results also indicate that the effects of these variables overshadow and distract from the impact of celebrity star power. While the third specification attempts to account for this correlation by excluding the production budget and number of screens, this relationship is tricky to untangle. In future studies, it may be beneficial to differentiate where the money in the production budget goes to in order to account for the casting budget.

The second major finding in this study is the major significance of the opening weekend revenues on box office success. Having a larger audience on opening weekend increases the word of mouth about the movie, signifying its popularity. This may also increase the popularity of the celebrities in the movie in the short term. In addition, the

23 study demonstrates the importance of viewer ratings on the success of a movie. Movies that had higher ratings on IMDB made more money in 2015, suggesting the viewer’s opinion about a movie is still a significant predictor of box office success. Although this result may seem obvious, it may help support one of the major theories behind the importance of star power: that having a star is in a film will increase the likelihood of a viewer recommending the film.

While this study focuses on the impact of celebrity on box office revenues, the results expose the major trends in types of movies for the year 2015. Even though many of the coefficients on the genre variables were insignificant, the results still suggest adventure and action movies were respectively the most and least successful genres. The popularity of adventure films is expected, as over the past 15 years, five of the top ten highest grossing films were adventures (The Internet Movie Database, 2016). It is slightly unexpected that action films were the least successful, however, the results suggest action movies performed poorly in 2015. The data shows movies that were sequels performed well in 2015. This too is expected; as over the past 15 years, eight of the ten highest grossing films were sequels. Similar to the genre, the MPAA rating of a film was a less significant predictor of box office success. Although the rating is not as influential as other predictors, the study still provides definitive results. Throughout all three specifications, movies rated PG performed the best while those ranked R performed the worst. These results support the theory that the larger the audience is, the more money the movie will make.

Additionally, the model provides several results as to the impact of star power on box office revenues. As expected, the more star power a film has, the more money the

24 film makes. On average, for each percentage decrease on the STARmeter ranking

(decreases signal more star power due to the inverse relationship of the system), the movie’s box office revenues increase by 2.5%. By using variables to account for the star power of the top celebrity, the second top celebrity and the combined total of the top three celebrities, the model provides additional results. In each specification the ranking of the second highest paid celebrity was the most significant and the greatest in magnitude. This finding may be a function of the given dataset or it may suggest having a second star in a film is the key to box office success. Although the measure of the top three celebrities’ star power was not very significant, increasing the size of the dataset may provide better support for Nelson’s ensemble effect.

Another important finding is the diminishing returns of star power to box office revenues. Recall that the initial specification of the model included variables to account for these hypothesized diminishing returns. As these coefficients were only somewhat significant it is difficult to assert that star power provides diminishing returns, however the reviewed literature does support this theory. This is not surprising, as over time the infatuation with one celebrity fades away as another becomes popular.

Finally, the model accounts for the role of public image on box office success.

Unsurprisingly, the total number of appearances increased box office revenues, and for each article written revenues increased by 2.8%. Likewise for each additional positive article appearance, revenues increased by 3.7% and for each additional negative article appearance revenues increased by 1.2%. Interestingly the number of positive article appearances had the greatest effect on revenues, which may suggest that a positive public image is important to viewers. Although the coefficients on negative publicity were

25 mostly insignificant, the model suggests they also increase revenues, implying all publicity is good publicity.

Overall, this study supports major findings from previous work on modeling box office hits. While the production budget of a film continues to be the biggest predictor of success, star power is indeed significant. The results suggest that hiring a second big name celebrity increases revenues. In addition, it is important for the leading actors to maintain a positive public image. Finally, while a positive public image increases revenues, all publicity is good publicity.

26 APPENDIX A

TABLE 2.1

TOP ACTORS/ACTRESSES IN 2015

Total Box Office Number of Academy Golden STARmeter Actor/Actress Revenues Article Award Globe Ranking (worldwide) Appearances Nominations Nominations

Robert $7,217,724,353 58 42 0 0 Downey Jr. Tom Cruise $7,121,110,240 35 36 0 0 Leonardo $6,344,884,098 36 9 1 1 Dicaprio Bruce Willis $5,072,601,361 23 113 0 0 Vin Diesel $4,933,158,413 53 34 0 0 Hugh $4,875,274,672 22 102 0 0 Jackman Matt Damon $4,858,294,689 24 19 1 1 Chris $4,664,251,540 42 6 0 0 Hemsworth Chris Evans $4,465,409,491 43 53 0 0 Jenifer $4,400,302,799 73 2 1 1 Lawrence Dwayne $3,710,191,396 29 56 0 0 Johnson Jeremy $3,628,356,427 26 71 0 0 Renner Bradley $3,398,505,150 21 109 0 0 Cooper George $3,303,412,909 35 102 0 0 Clooney Mark $2,849,713,930 18 76 0 0 Wahlberg Morgan $2,190,638,662 16 95 0 0 Freeman Channing $1,794,395,703 25 92 0 0 Tatum Shailene $882,400,757 15 94 0 0 Woodley Chris Pratt $837,997,219 49 2 0 0 Melissa $786,983,658 41 82 0 1 McCarthy Source: author’s calculations.

27 APPENDIX B

TABLE 4.1

STAR POWER MEASURES

STARmeter Positive Negative Total Actor/Actress Ranking Publicity Publicity Publicity Lowest STARmeter Ranked Stars: Chris Pratt 2 23 0 49 Jenifer Lawrence 2 32 7 73 Chris Hemsworth 6 28 1 71 Josh Hutcherson 7 15 2 36 Daniel Craig 17 15 3 31 Jamie Dornan 17 7 3 35 Anna Kendrick 18 27 3 59 Matt Damon 19 7 2 29 Paul Rudd 31 11 0 28 Vin Diesel 34 22 0 53 Highest STARmeter Ranked Stars: Hannah Ware 2682 0 0 0 Rupert Friend 1022 2 0 6 Luke Bracey 980 3 1 7 Edgar Ramirez 938 0 0 6 Diane Keaton 899 2 0 7 Jeremy Irvine 893 0 0 0 Sharlto Copley 821 0 0 4 Dev Patel 721 0 1 4 James Ransone 714 0 0 1 Emory Cohen 707 0 0 2 Source: author’s calculations.

28 APPENDIX C

TABLE 4.2

SUMMARY STATISTICS

Standard Variable Mean Minimum Maximum Deviation BoxOffice 96,900,000 119,000,000 19,400,000 740,000,000 Budget 71,200,000 65,100,000 100,000 300,000,000 Screens 3177.94 672.754 947 4311 OpeningWeeke 32,200,000 41,600,000 187,281 248,000,000 nd Action .2 .402 0 1 Adventure .07 .256 0 1 Comedy .23 .422 0 1 Horror .09 .287 0 1 Thriller .14 .348 0 1 PG .17 .377 0 1 PG13 .48 .502 0 1 R .32 .468 0 1 Sequel .22 .416 0 1 Rating 6.61 .998 3.9 8.5 Star1 242.54 249.77 2 1022 Star2 305.39 349.98 6 2682 Startot 1,853.2 2,651.34 186 12,871 Star12 120,585.7 238,689.6 4 1,044,484 Star22 214,526.2 748,882.9 36 7,193,124 Startot2 12,848,615.3 2,762,839.2 34,596 165,662,641 Npub1 1.67 2.49 0 12 Npub2 1.21 2.57 0 14 Ppub1 6.56 7.01 0 32 Ppub2 4.34 4.89 0 23 Tpub1 14 12.85 0 71 Tpub2 19.67 16.18 0 73 Source: author’s calculations.

APPENDIX D

TABLE 5.1

RESULTS FOR IMPACTS ON BOX OFFICE REVENUES

Variable Model 1 Model 2 Model 3 LogBudget .414*** .342***

(.249) (.043) LogScreens .645*** .688***

(.204) (.195) LogOpeningWeeken .122** .279*** .358** d (.074) (.072) (.136) Action -.236* -.513** -.311* (1.42) (.240) (.120) Adventure .337* -.324 .471*** (.203) (.316) (.163) Comedy .087** .234* .449*** (.439) (.136) (.124) Horror -.285** .351** .256* (.146) (.184) (.128) Thriller .382 .056 -.290** (.571) (.044) (.126) PG -.172 .291* .346 (.701) (.149) (.213) PG13 -.061* -.092 -.015* (.041) (.128) (.008) R .142** -.466** -.403* (.071) (.189) (.212) Sequel -.237* .263** .284** (.182) (.105) (.127) Rating .171** .304* .310** (.086) (.173) (.121) Star1 1.76

(.98) Star2 -3.17*

(1.91) Startot -1.29

(.841) LogStar1 -.146** -.211**

(.065) (.081) LogStar2 -.239** -.438**

(.117) (.148) LogStartot -.304* -.198*

30 (.173) (.112) Star12 4.27*

(2.61) Star22 6.81**

(3.43) Startot2 3.45

(2.23) Npub1 .012* .007 .011 (.007) (.012) (.016) Npub2 .004 -.023 .050 (.012) (.042) (.036) Ppub1 .025** .038** .042** (.007) (.012) (.083) Ppub2 .057 .007* .056* (.099) (.012) (.095) Tpub1 .021* .032* .045** (.019) (.053) (.089) Tpub2 .015* .027** .029** (.029) (.053) (.057) Standard errors are in parentheses, *** p < 0.01, **p < 0.05, *p < 0.1 Source: author’s calculations.

Appendix E

Figure 5.1. Normality plots of the variables Star1, Star2 and Startot before and after being transformed by logarithms.

1.00 1.00 0.75 0.75 0.50 0.50 Normal F[(Star1-m)/s] Normal Normal F[(logStar1-m)/s] Normal 0.25 0.25 0.00 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Empirical P[i] = i/(N+1) Empirical P[i] = i/(N+1) 1.00 1.00 0.75 0.75 0.50 0.50 Normal F[(Star2-m)/s] Normal Normal F[(logStar2-m)/s] Normal 0.25 0.25 0.00 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Empirical P[i] = i/(N+1) Empirical P[i] = i/(N+1)

1.00 1.00

0.75 0.75

0.50 0.50

Normal F[(Startot-m)/s]Normal Normal F[(logStartot-m)/s] Normal 0.25 0.25

0.00 0.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 Empirical P[i] = i/(N+1) Empirical P[i] = i/(N+1)

Source: author’s calculations.

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