The Determinants of Domestic Box Office Performance in the Motion Picture Industry

This paper examines the determinants of box office revenue in the motion picture industry. The sample consists of 505 films released during 2001-2003. Regression results indicate the primary determinants of box office earnings are critic reviews, award nominations, sequels, Motion Picture Association of America rating, budget, and release exposure. Specific results include the observation that a ten percent increase in critical approval garners an extra seven million dollars at the box office, an academy award nomination is worth six million dollars, the built in audience from sequels are worth eighteen million dollars, and R-rated movies are penalized twelve million dollars. INTRODUCTION The movie industry earns over eight billion dollars annually at the domestic box office and employs over 580,000 people in the areas of film production & services, theaters, and home video (Simonoff & Sparrow, 2000). A single movie can be the difference between millions of dollars of profits or losses for a studio in a given year. Film audiences make hits or flops and they do it, not be revealing preferences they already have, but by discovering what they like (DeVany & Walls, 1996). When they see a movie they like, they make a discovery and they tell their friends about it. Film critics can be viewed as friends that initially view movies for fans and play an integral role in the information cascade that generates a hit. The recent admission of using fabricated film reviews by Sony Pictures, 20 Century Fox, Artisan Entertainment, and Universal Pictures provides anecdotal evidence that industry executives believe that reviews and testimonials have an impact at the box office. In the most extreme case, Sony Pictures admitted to marketing fraud in 2001 by using an imaginary film critic to promote two new releases. The purpose of this research is to analyze the motion picture industry with a focus on how box office performance is impacted by film reviewers, film classification (sequel, rating, and genre), industry awards, and historical fundamentals (budget and number of screens showing the film in wide release). This paper is divided into four sections. First, a survey of the related literature is discussed. The second section provides the model specification. This is followed by an empirical evaluation of the determinants of box office performance for 505 films released during 2001-2003. The final section offers concluding remarks. Southwestern Economic Review 138 SURVEY OF THE LITERATURE Many researchers have developed models that explore the potential determinants of motion picture box office performance. Litman (1983) was the first to develop a multiple regression model in an attempt to predict the financial success of films. The original independent variables in the landmark work include movie genre (science fiction, drama, action-adventure, comedy, and musical), Motion Picture Association of America rating (G, PG, R and X), superstar in the cast, production costs, release company (major or independent), Academy Awards (nominations and winning in a major category), and release date (Christmas, Easter, summer). Litman’s model provides evidence that the independent variables for production costs, critics’ ratings, science fiction genre, major distributor, Christmas release, Academy Award nomination, and winning an Academy Award are all significant determinants of the success of a theatrical movie. Litman and Kohl (1989) and Litman and Ahn (1998) have replicated and expanded the initial work of Litman (1983). One area of interest has been the role of the critic. The impact of the critic has been approached many ways yielding different results, although the majority of studies find that critics play a significant role on the success or failure of a film. Eliashberg and Shugan (1997) divide the critic into two roles, the influencer and the predictor. The influencer is a role where the critic will influence the box office results of a movie based on his or her review of the movie. Eliashberg and Shugan’s results suggest that critics do have the ability to manipulate box office revenues based on their review of a movie. The predictor is a role where the critic, based on the review, predicts the success of a movie but the review will not necessarily have an impact on how well the movie performs at the box office. Eliashberg and Shugan show that the predictor role is possible but does not have the same statistical evidence as the influencer role. Reinstein and Snyder (2000) focus on the critics Siskel and Ebert and how their reviews impact box office success. The authors report that the correlation between good movie reviews and high demand might be false due to unknown quality measurements. In order to circumvent the proposed false correlation Reinstein and Snyder apply a “differences in differences” approach that yields a conclusion that positive reviews have a surprisingly large and positive impact on box office revenue. Reinstein and Snyder also report that their results show that the power to influence consumer demand does not necessarily lie in the entire critic population, but may lie in the hands of a few critics. Wallace, Seigerman, and Holbrook (1993) employ a sample of 1,687 movies released from 1956 through 1988 to investigate the relationships between movies box office success and critic ratings. They find a poorly rated movie will actually lose money for every positive review it receives while a highly rated movie will continue to gain money for every positive review it receives. Wallace, Seigerman, and Holbrook (1993, p. 11) interpret these findings by saying that “it appears that a bad movie has something to gain by being as trashy as possible. ... [For] a good movie, it apparently pays to strive for even greater excellence.” Ravid (1999) has also looked at movie reviews as a source of projecting higher revenues. He concludes that the more reviews a film receive, positive or negative, the higher revenues it will obtain. Although much research has shown that the critic is a positive indicator of box office success others have shown that the critic plays a much less important role. The Determinants of Domestic Box Office Performance In the Motion Picture Industry 139 Levene (1992) surveyed students at the University of Pennsylvania and concludes from her 208 useable surveys that positive critic reviews ranked tenth, behind plot, subject, and word-of-mouth on a list of factors that influence the decision to watch a film. Levene’s study reveals that theatre trailers and television advertising were the two most important determinants. Faber and O’Guinn (1984) conclude that film advertising, word-of-mouth and critics’ reviews are not important compared to the effect that movie previews and movie excerpts have on the movie going public. Wyatt and Badger (1984) find that negative or positive reviews have little effect on the interest of an individual to see a movie over a mixed review or seeing no review. Further research by Wyatt and Badger (1987) conclude that positive reviews and reviews that contain no evaluative adjectives, which they called non-reviews, are deemed more interesting than a review that was negative or mixed. More recently, Wyatt and Badger (1990) report that reviews containing high information content about a movie raise more interest in a film than a positive review. Research has shown a seasonal pattern in movie releases and box office performance. Litman (1983) reports that the most important time for a movie release is during the Christmas season. Sochay (1994) counters this with evidence that the summer months are the optimal time of year to release a motion picture. Sochay, referencing Litman (1983), explains his conflicting results are due to competition during the peak times. Sochay adds that the successful season will shift from the summer to Christmas in different years due to film distributors avoiding strong competition. Radas and Shugan (1998) developed a model that captures the seasonality of the motion picture industry and apply it to the release of thirty-one movies. The authors find that the length of a movie release on average is not longer during the peak season but peak season movies typically perform better at the box office. Einav (2001) investigates seasonality in underlying demand for movies and seasonal variation in the quality of movies. He finds that peak periods are in the summer months and the Christmas season because distributors think that is when the public wants to see movies and when the best movies are released. He comments that distributors could make more money by releasing “higher quality” movies during non-peak times because the movie quality will build the audience and there will be less competition than at peak times. Film ratings passed down from the Motion Picture Association of America (MPAA) may also influence box office performance. Many film companies fight for a better rating, often re-shooting or re-editing scenes multiple times in order to get their preferred rating, most often being PG or PG-13 because these ratings exclude virtually no one from seeing the movie. Sawhney and Eliashberg (1996) develop a model where the customer’s decision-making process on whether to see a movie can be broken into a two-step approach, time-to-decide and time-to-act. The results of their study show that movies with an MPAA rating of restricted (rated R) perform worse at the box office than movies without a restricted rating. The analysis shows that restricted rated movies have a higher time-to-act but have longer time-to-decide periods than family movies. Ravid (1999) provides evidence from a linear regression model that G and PG rated films have a positive impact on the financial success of a film. Litman (1983) on the other hand, finds that film ratings are not a significant predictor of financial success. Austin (1984) and Austin and Gordon (1987) also look at film ratings in an attempt to find a correlation between ratings and movie attendance but find no significant relationship. Southwestern Economic Review 140 Anast (1967) was the first to look at film genre and how it relates to film attendance. His results show that action-adventure films produce a negative correlation with film attendance while films containing violence and eroticism had a positive correlation. Litman (1983) shows that the only significant movie genre is science fiction. Sawnhey and Eliashberg (1996) use their two-step approach and find that the drama genre has a slower time-to-act parameter while action movies result in a faster time-to-decide than other movie genres. Neelamegham and Chinatagunta (1999) employ a Bayesian model to predict movie attendance domestically and internationally. They find that across countries the thriller genre is the most popular, while romance genre was the least popular. Awards are important to every industry but few industries experience financial compensation from an award more than the motion picture industry. Litman (1983) shows that an Academy Award nomination in the categories of best actor, best actress, and best picture is worth $7.34 million, while winning a major category Academy Award is worth over $16 million to a motion picture. Smith and Smith (1986) point out that the power of the Academy Award explanatory variable in models explaining patterns in movie rentals will change over time as the effects of different Academy Awards could cause both positive and negative financial results to a movie in different time periods. Nelson, Donihue, Waldman, and Wheaton (2001) estimate that an Academy Award nomination in a major category could add as much as $4.8 million to box office revenue, while a victory can add up to $12 million. The authors find strong evidence toward the industry practice of delaying film releases until late in the year as it improves the chances of receiving nominations and monetary rewards. Dodds and Holbrook (1988) look at the impact of an Academy Award after the nominations have been announced and after the award ceremony. The authors find that a nomination for best actor is worth about $6.5 million, best actress is worth $7 million and best picture is worth $7.9 million. After the award ceremony the best actor award is worth $8.3 million, best picture is worth $27 million, and best actress award is not statistically significant. Simonoff and Sparrow (2000) find that for a movie opening on less than ten screens an Academy Award nomination will increase the movies expected gross close to 250% more than it would have grossed if it had not received the nomination. For movies opening on more than ten screens, an Academy Award nomination will increase the movies gross by nearly 30%. DATA AND MODEL Predicting the performance of new feature films is widely regarded as a difficult endeavor. Each film has a dual nature, in that it is both an artistic statement and a commercial product (Sochay, 1994). Knowing what factors and conditions affect the performance of theatrical movies is of great value for the eight billion dollar a year industry. Many studies have attempted to estimate the determinants of box office performance by employing empirical models to a limited number of high profile features. The approach of this study is unique because the data set is derived from a cross section of all movies released in the years 2001, 2002, and 2003 that opened in twenty-five or more theatres or eventually reached an audience at one hundred theaters or more. Less than eighty movies in the universal sample for 2001The Determinants of Domestic Box Office Performance In the Motion Picture Industry 141 2003 did not meet the criteria of opening in twenty-five or more theatres or reaching one hundred or more theaters. A total of 505 motion pictures are in the final sample. The primary source of data for this study is the Rotten Tomatoes website (rottentomatoes.com). The website is a unique rating system that summarizes positive or negative reviews of accredited film critics into an easy to use total percentage that is aggregated for each motion picture. In addition to providing a system of aggregate reviews, the website also contains information pertaining to revenue, release date, movie rating, genre, and number of screens featuring a film each week of release. WorldwideBoxoffice.com, Movies.com, and the-numbers.com are three additional sources of data and information. The empirical model employed to investigate the determinants of box office performance for this study is specified below as: (1) REVENUEi = B0 + B1CRITICi + B2HOLIDAYi + B3ADULTi + B4SEQUELi + B5ACTIONi + B6CHILDRENi + B7AWARDi + B8RELEASEi + B9BUDGETi + ui, where REVENUE is domestic gross box office earnings, CRITIC is the percent approval rating for a film by an agglomeration of film critics, HOLIDAY is a categorical variable representing movie releases around a major holiday (Memorial Day, Independence Day, Thanksgiving, Christmas, and New Year’s), ADULT is a categorical variable for movies with a restricted rating (Rated R), SEQUEL is a categorical variable for movies that are derived from a previously released film, ACTION is a categorical variable for movies in the genre of action/adventure, CHILDREN is a categorical variable for movies in the genre of children’s movie, AWARDS is the number of Academy Award nominations a film receives, and RELEASE is the number of theaters showing the film during the week of wide release. BUDGET controls for the estimated production and promotion costs for each movie. Several alternative model specifications were considered including control variables for independent films, presence of an established star actor or director, winning an Academy Award, and new release competition. Inclusion of these variables into the model affected the standard errors of the coefficients but not the value of the remaining coefficients or they suffer from excessive multicollinearity with variables included in the model. For these reasons they are not included in the final model. Descriptive statistics for the model variables are presented in Table 1. Average movie revenue in the sample is $46 million, with a maximum of $404 million (Spider-Man) and a minimum of $109 thousand (Waking up in Reno). Average critical rating of the movies in the research cohort is approximately 50 percent (49.85) with a standard deviation of 27 percent. Twenty-four percent of the movies in the sample are holiday releases, 31 percent have a restricted rating, 12 percent are sequels, 18 percent are action films, and 11 percent target children. The maximum number of Academy Award nominations is 13 (Chicago). The average release for movies in the sample reached 1,783 theaters during the week of wide release, with a maximum of 3,782 theatres and a minimum of 25 theaters (the minimum allowed in order to be included in the data set). The budget for movies in the research sample varies from a low of $150 thousand (Tadpole) to a high of $170 million (Terminator 3: The Rise of the Machines). Southwestern Economic Review 142 Table 1 Summary Statistics: Domestic Box Office Revenue (2001-2003) Variable Mean Maximum Minimum Standard Dev. REVENUE 46,099,483 403,706,000 109,000 51,263,335 CRITIC 49.85 97 0 27.23 HOLIDAY 0.24 1 0 0.40