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 By 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 Signature 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,