Master's Thesis - Factors Affecting Theatrical Success of Films: An Empirical Review

Factors Affecting TheatricalSuccess of Films: An EmpiricalReview

SIDDHARTH UPASANIGOKHALE INSTITUTE OF POLITICS & ECONOMICS

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Declaration

I, hereby undersigned, affirm that this study has been done solely by me, as a Master’s Thesis

course in the partial fulfillment of the requirements for the degree of Master’s in Arts in

Economics from Gokhale Institute of Politics and Economics, Pune.

April, 2013 Yours faithfully,

(Siddharth Upasani)

We, hereby undersigned, confirm that this study has been completed by the above mentioned

student independently under our guidance, only for the fulfillment of Master’s in Arts in

Economics from Gokhale Institute of Politics and Economics, Pune.

Dr. Debasish Nandy Dr. Pradeep Apte

(Guide) (Co-Guide)

Gokhale Institute of Politics & Economics Gokhale Institute of Politics & Economics

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Acknowledgements:

I would like to thank my principle advisor, Dr. Debasish Nandy, for allowing me to work under him. His guidance, valuable time and insights were extremely helpful, and without them it would have been difficult to successfully complete this thesis.

Siddharth Upasani

April, 2013

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MASTER’S THESIS:

Factors Affecting Theatrical Success of Films: An Empirical Review

Siddharth Upasani

M.A. Economics, Part II

Roll Number: ECO-1133

Gokhale Institute of Politics & Economics

April 10, 2013

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Abstract

The Indian film and television industry’s combined revenues neared $8 billion in the year 2008, with the figure predicted to reach $13.2 billion [or Rs.60, 000Cr] by

the end of this year. The film industry, on its own, employs more than 4 lakh people1, with each passing year adding more and more numbers as India

continues to churn out films at a rate unmatched by any other country in the world. In such a competitive and unforgiving environment, it’s only natural that producers of films try and replicate past successes to ensure the survival of their production houses and rake in profits. However, the theatrical performance of

the majority of films defies the previously established logical routine, and requires a careful examination of the factors at work. This study attempts to evaluate

whether a film’s theatrical success can be predicted, using the Hindi film industry to construct relevant Logit models.

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Contents:

Introduction - 8

An Overview of the Literature - 10

Inclusion & Exclusion of Factors - 14

Model & Variable Specification - 22

Empirical Findings - 25

Concluding Remarks - 37

Appendix - 38

Notes - 42

References - 44

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List of Tables and Figures [in order of appearance]:

Figure 1: Weekly Net Revenue Collections Table 1: Film Variable Names and Definitions Figure 2: K-density graph, netrevenues Figure 3: Normal Probability Plot, netrevenues Figure 4: K-density graph, ln_netrev Figure 5: Normal Probability Plot, ln_netrev Tables 2-8: Correlation matrices Table 9: Linear Regression Model, output no. 1 Table 10: Linear Regression Model, output no. 2 Table 11: Linear Regression Model, output no. 3 Table 12: Linear Regression Model, output no. 4 Table 13: Binary Logit Model, output no. 1 Table 14: Binary Logit Model, output no. 2 Table 15: Binary Logit Model, output no. 3 Table 16: Binary Logit Model, output no. 4 Table 17: Ordered Logit Model, output no. 1 Table 18: Ordered Logit Model, output no. 2 Table 19: Simultaneous Quantile Regression Model, output summary Table 20: Simultaneous Quantile Regression Model, outputs 1-10

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I. INTRODUCTION

India’s first feature film, Raja Harishchandra, enters its centenary year this year. A silent film, it was another 18 years before the first sound film, Alam Ara, was made. By the 1930s, the film industry was making more than 200 films annually2.

Fast forward a hundred years, and the Indian film industry is scaling new heights. Indian producers are financing Oscar-winning films being made by American directors for an international audience, while Indian actors are collaborating with acclaimed international artists on a regular basis. Amidst this trans-national exchange of cultural and artistic identities, skills and values, the Indian film industry continues to evolve, constituting, arguably, the single biggest influence on the average Indian’s life, ahead of cricket.

With such a dynamic and intelligent system in place, it’s rather surprising to see the calendar year littered with films which don’t even come close to recovering their investments. However, there exists a smaller group of films which have captured the imagination of the populace not only on the strength of their entertainment value, but by the numbers associated with their box-office revenues. At a time when households are tightening their belts due to rising prices, films threaten to break theatrical earning records on a regular basis. Is the film industry recession-proof? Unfortunately, that is not the question which this study will try and answer. Instead, the study focuses on the factors which have a bearing on their box-office success.

Most would conclude that a large production cost would generate high revenues for a film. This is the basis of the blockbuster theory, which roughly states that production houses should spend large amounts of money while making a film as it then has a greater chance of earning large profits[or, becoming a blockbuster], allowing it to cover the costs of several failed projects by the same production house. The underlying logic is that a film with a larger budget has a greater probability of generating a large profit than a film with a smaller budget. Consider the example of 20th Century Fox which was struggling in the mid-1970s, but decided to back George Lucas and his trans-galactic science-fiction saga, The Star Wars, by spending more than $10 million in 1977 on the first installment, The Empire Strikes Back. The rest, as they say, is history, with George Lucas selling The Star Wars franchise rights [as part of his company Lucasfilm] to Disney for approximately $4 billion recently.

However, if production cost was the sole determinant of film revenues, how does one explain the catastrophic performance of films such as The Green Lantern, or closer to home, Players? Both were financed sufficiently and had popular actors in the lead roles. However, their performance, or the lack of it, means that a more careful examination is required.

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This study inspects the influence exerted by several factors on the theatrical success of films by constructing relevant logit models. Specifically, it considers the Hindi film industry due to its large audience as against any regional language based film industry. The sample consists of 321films released in the years 2008, 2009, 2010 and 2011.

The paper is organized in the following manner. Section II reviews the existing literature relevant to the topic at hand. Section III discusses the inclusion of the various variables in the model. Section IV makes arguments in support of the model chosen. Section V presents the results, and Section VI presents the concluding remarks.

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II. AN OVERVIEW OF THE LITERATURE

Studies on the film industry can be broadly categorized into two: studies using the economic approach, and studies using the communication theory approach.

Studies of the latter variety try to investigate the reasons behind a person’s choice of films as a form of entertainment, instead of the other available options like a play, circus, amusement park, et cetera. Once a person has chosen films as his/her preferred form of entertainment, studies using the communication approach can also try and explain why an individual chooses a certain film over others. Such studies are conducted with the help of surveys which require individuals to report their practices and preferences.

Studies using the economic approach, meanwhile, investigate which economic factors influence theatrical attendance of films, and by extension, film revenues. Studies of this type vary depending on their focus. Some inspect the impact of particular variables[such as genre of the film, or age certificate] on theatrical success, while others try and incorporate all the relevant variables into a more general model with a view to forecasting financial success of films.

As this study looks at factors influencing box-office success, the existing literature examined will be relevant to the economic approach.

In studies which examine the impact of a particular variable on box-office success, the variables generally considered are star power, release dates of films, film reviews and advertising budgets.

The presence of stars is probably the most important indicator that the producers/directors of the film are trying to achieve commercial success for their films. What makes things interesting is the different treatment given to star power - in terms of its definition - in different studies. Wallace, Seigerman, and Holbrook [1993]3 define a star as an actor/actress who has appeared in at least seven films. The study’s data set consisted of 111 such actors, each of them represented by a dummy variable. The remaining explanatory variables were year of release, quality rating, age-appropriateness [CineBooks rating 1-6], country of make, length in minutes, genre [25 different genres], and finally, the production cost. The study concluded that only 24 of the 111 actors and actresses had a positive significant impact on a film’s rental income.

Another definition of star power is given by DeVany and Walls [2004]4, who define a lead actor being one if he or she appears on Premier’s annual rating list of the hundred most powerful people in Hollywood, or on James Ulmer’s list of A and A+ actors. The authors use this definition of star power to explain the ‘’curse of the superstar’’ – the phenomenon where the

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presence of a star actor or actress does lead to a film garnering larger profits, but at the same time, the wages needed to pay the star nullifies any additional revenue generated by the presence of the star in the film. The authors found that the probability of a film without a star earning a profit greater than $20 million is .02, while the probability is .01 for a film with a star. Films with stars also tend to have a slightly higher probability of earning a profit, while films without stars have a higher probability of making a loss.

Timing, in films, like in most other fields, is an important contributing factor. A film released on a holiday can expect a larger audiences compared to one released on a working day. A film released during the holiday season can expect sustained attendance figures compared to a film released some time else.

Einav [2007]5 classifies the release date of a film using 56 dummy variables for the different weeks of the year, with additional weeks being added to account for the movement of holidays. Einav concludes that the weeks of Memorial Day [a United states federal holiday which occurs every year on the final Monday of May,it is a day of remembering the men and women who died while serving in the United States Armed Forces], Independence Day, Thanksgiving, and the weeks before and after Christmas, all, have a significant impact on theatrical revenues.

Other studies have included two dummy variables – one for films released during the summer season, and the other for films released during holiday weeks. Both variables have tended to be significant, with their magnitudes varying depending on the variables included in the model.

Holbrook and Addis [2007]6, in their study, focus on the quality of films and the impact of expert judgment [from professional reviews] versus ordinary evaluation [from people providing criticism on IMDb] on domestic box-office success. Holbrook and Addis find that there is a significant, but weak, relationship between expert judgment and domestic success.

Gesmer, Oostrum and Leenders [2006]7, using data on box-office success of films in Holland, examine the impact of film reviews on theatrical success. The authors, from their study, propose two effects: one, the influence effect, which holds for art-house or independent films, in which film reviews have the greatest impact on theatrical success. The second effect is the predator effect, and it is associated with mainstream films. Here, film reviews do not have such a great influence over the audience’s choice of films. Instead, the audience is attracted by other forms of information, like advertisements. The authors also find that media reviews are not influencers of the mainstream cinema going public. Instead, they are predictors of the theatrical success of the films. They conclude that films positively reviewed in the media don’t influence choices of the public, but the positive reviews just happen to coincide with theatrical success.

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Deuchert, Adjamah and Pauly [2005]8 arrive at a film’s quality by the number of Oscar nominations it receives. The authors find that there isn’t a big difference in total revenues for films that win an Oscar and those who are just nominated for one. Hence, a nomination is viewed as an indicator of a film’s high quality.

The inclusion of a variable like advertising budget is difficult due to the unavailability of accurate and reliable data. However, Elberse and Anand [2007]9 try to circumvent this problem by using a film’s stock price as it trades on the Hollywood Stock Exchange [an online market simulation] to study the impact of film advertising. The authors find that advertising has a positive significant effect on expected revenues, with the impact varying across films of differing quality – implying that the return to advertising for low-quality films is negative.

Returning to studies which incorporate several variables in their models, the focus shifts from a particular variable to the impact of variables on a film’s success.

Smith and Smith [1986]10 were the first to publish their preliminary analysis in an economics journal. They took a data set of films that earned the highest rental payments [amount left over after the cinema owners have taken their share of box-office revenues] and attempted to explain the success of these films on the basis of Oscar nominations and awards and the year of their release. The authors found different results for the three decades of their sample data, and interpreted this as the effect of changing tastes.

Prag and Casavant [1994]11 extended the above mentioned study, both in terms of number of observations and independent variables used. The variables they included were critical acclaim, cost of production, MPAA rating and genre for each film, impact of star performers, and two dummies to represent sequels. Their study suggested that film quality, star power, sequels, Oscar awards and the MPAA ratings, all, ha a significant positive impact on film revenues. Drama was found to be the only significant film genre, and had a negative impact on revenues.

Liman and Kohl [1989]12 attempted to predict the rental income with the following variables included in their model: MPAA ratings, country of origin, star power, production budget, critical reviews, genre [15 categories], sequels or based on well-known ideas, brand [distribution company], release date, pattern of release [number of screens a film opened on] and market forces [market shares and admission tickets].Litman and Kohl found that none of the MPAA ratings were significant, and the only significant genres were science-fiction/fantasy and drama. Additionally, star power was found to be significant.

DeVany and Walls [1996, 1999, 2001, 2002]13 and DeVany and Lee [2001]14 concluded from their studies that the distribution of film revenues is highly skewed, with a few ‘’blockbuster’’ films garnering the major share of total revenues, while others fail.

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Collins, Hand and Snell [2002]15used DeVany and Walls’ approach for the United Kingdom andcalculated the probability of a film being a hit theatrically. Their findings suggested a strong relationship between star power, film reviews and theatrical success, with only half the genres proving to be significant, their impacts varying.

In essence, previous studies have tried to explain the theatrical performance of films using their attributes as explanatory variables. The majority of these studies have been for the North American film industry [the USA specifically], with a few for a few European counties’ film industries. Bar a recent study conducted by students of IIM-Ahemdabad16, no such study has been conducted for the Indian [Hindi, in this case] film industry.

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III. INCLUSION& EXCLUSION OF FACTORS

While deciding on the factors which can influence theatrical success of films, it’s important to remember the features of films. Films are one-off, unique products, and have a shelf life of a few weeks at the most. They enter and exit the market for films continuously, and any given film competes against a continuously changing set of rivals.

Keeping these features in minds, there are two categories of variables which could be included in our model. The first category would include those variables which are external to the film industry, such as prices of complementary items like popcorn, soft drinks and other eatables available in cinemas, along with the price and availability of forms of entertainment which are substitutes to films. However, these are factors which would effect overall consumption of films, and not help us decide which film is preferred over the rest, and therefore, succeeds theatrically in comparison to its rivals.

The second category of factors would include attributes of films such as presence of stars, genre, et cetera. These are the attributes which influence the audience’s decisions when it comes to choosing which film to watch from the options available.

Cost of Production:

Following from the blockbuster theory, a film with a higher production and post-production budget can be expected to earn more than a low budget film due to various reasons. An expensive film has a natural excitement which attracts the audience after whetting their curiosity. A large budget allows the directors to attract the best actors – who have their own fan following – to increase the hype surrounding the film, employ the best technicians if the film requires computer graphics [which are increasingly becoming more popular], and promote the film widely using different forms of media. Such a concerted effort has an effect which can only be called the ‘’opening week effect’’.

Considering 321 films released during the concerned period of 2008-2011, it’s clearly visible that the opening couple of weeks are extremely important when it comes to theatrical collections.

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Figure 1

Source: BoxOfficeIndia.com

Hence, a large production and post-production budget can be instrumental in attracting audiences in the opening week, while the element of curiosity is still present.

However, there are several problems associated with the inclusion of production costs as an explanatory variable in a model. Firstly, reliable data on film budgets is generally not available as producers do not disclose such details. Second, the inclusion of a film’s budget – production and post-production costs – in a model can lead to the problem of multicollinearity. An expensive film would naturally include star actors in the lead roles, along with a substantial post-production outlay, which would include promotional costs. For this reason, both, production costs and promotional expenses have not been included in the model.

Star Power:

Once could say, without conducting any study, that its largely due to big, renowned actors that films attract the audiences they do. True, the odd film might work due to its extraordinary story or other fascinating attributes, but when we talk of high revenue films, they come associated with big name actors with a tremendous fan following, capable of pulling the public to theatres purely because they are part of the film. The quality of the film and the storyline are rendered irrelevant. This is mainly because of the standing actors enjoy in India. The roots of this hero-worshipping tradition can be traced back to 1950s and 1960s, with Rajesh Khanna being the

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

1 2 3 4 5 6

Wee

kly

Net

Rev

enue

Col

lect

ion,

in

cror

es o

f rup

ees

Week of running in theatres

Weekly Net Revenue Collections

Weekly Net Revenue Collections

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first superstar, making the audiences swoon with his every move. However, it wasn’t until the 1970s that the legend of the superstar was created with Amitabh Bachchan. Playing the angry, young man, pioneered by playwright John Osborne with his thundering character Jimmy Porter in the play Look Back In Anger, Bachchan came to personify the desires of every child, husband, wife and parent. To discuss his effect on Indian cinema would require a whole thesis by itself, but it should suffice to say that popular actors have the ability to attract sections of the public irrespective of the film they are part of. A John Abraham film would appeal to female audiences, while a Katrina Kaif starrer would bring in hordes of young males. Someone like Aamir Khan, who enjoys a unique mixture of critical and popular acclaim, would be expected to bring in the youth and older members of the public alike, and therefore, generate the most revenue for his films due to his wide ranging appeal.

In short, the actors and actresses that appeal to the most people – whether by age groups, gender, or any other criteria – will draw the most revenue for their films. Of course, it has to be mentioned that a completely hopeless storyline and script which does not even hold the audience’s attention could result in a flop, even with star actors.

To account for the presence of established stars in films, a dummy variable has been included, with the value of 1 indicating the presence of a star, and 0 indicating the absence. The problem is now one of defining which performers can be called stars in our model. Various studies have defined stars by the number of films they have acted in, or by the number of high grossing films they have been a part of, or how high they are ranked on critics’ lists of star actors and actresses. We shall define a star as an actor who has commanded a lead role in at least seven Hindi films in the last ten years. The additional clause of a ten year qualifying time period makes sure that stars of a bygone era are not counted as ‘’current stars’’ if they try and make a comeback.

Genre:

Like the leading actors employed by producers and directors, a film’s genre can play a role in attracting audiences. An animated film is likely to bring in children, while an action-packed film would find favour with the male youth. The genre which can interest the largest number of people will naturally attract the largest audience. Hence, keeping the demographic structure and its preferences in mind can work in favour of a producer.

Clearly defining a film’s genre can be a tricky exercise, purely because of the various elements a film might pack in hundred odd minutes. Providing a separate category for every unique treatment would be futile, making it essential that the genre is broad in its definition, while at the same time, is able to capture the essence of a film’s appeal to the audience in terms of its

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storyline. This study categorises films into seven genres: animated, drama, action/adventure, horror, romance, romantic-comedy, and comedy. Obviously, it’s possible that a film might cross genres, but a dominant genre can still be discerned. Each genre has been assigned its own dummy variable.

Censor Board Ratings:

The age-appropriateness certificate given to films by the Central Board of Film Certification is the deciding factor when it comes to films being available to particular audiences. An adult film, given an A certificate, would naturally restrict the film to people over the age of 18, while a film with a universal certificate [U], would be indicative of the fact that its been deemed appropriate for children too, and is allowed for unrestricted public exhibition throughout India, suitable for all age groups. A film with a UA certificate is not restrictive, but comes with an advisory notice that children below 12 be accompanied by a parent as the theme or content may be considered intense or inappropriate for young children. Now, it may seem obvious that a film with a U certificate would have the largest potential audience. However, it’s entirely possible that such a film might not attract a certain section of the public which interprets the U certificate as an indication of the film’s theme being childish or frivolous. Additionally, a more restrictive certificate may increase interest in a film, as suggested by Austin [1980]17.

The U, UA, and A certificates have been assigned separate dummy variables.

Reviews:

The inclusion of a variable which tries to quantify a film’s quality can be highly controversial and difficult. Yes, it’s possible that some films are better than others when it comes to acting performances, special effects, sets, costumes, and other relevant aspects. However, many of these differences can be explained by a film’s budget. A film made on a small budget would not be able to incorporate all items which would make it aesthetically and critically pleasing. However, other aspects, like direction, storyline, and the general manner of film-making cannot always be explained by a film’s budget. Hence, a factor like film reviews can help us distinguish between films of varying qualities. Film critics like the late Roger Ebert, who could singularly influence a film’s theatrical success with his trademark ‘’thumbs up/down’’, have been taken as indicators of a film’s quality in various studies. However, finding a parallel like Roger Ebert in the Hindi cinema critics circle is not possible. Ebert was the first film critic to win the Pulitzer, and directors like Christopher Nolan developed their keen analysis of films while growing up, reading his reviews ever since they were first published in the mid-1960s. It is for this reason

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that a single film critic has not been chosen to act as an indicator of film’s quality in this study.Instead, ratings given to films by viewers [on the Internet Movie Database, or www.imdb.com]have been taken as indicators of film quality. The ratings are out of 10 in ascending order of quality.

The reasons for taking a viewer-decided rating system as a proxy for film quality are multiple. Firstly, the ratings given by Indian critics are highly dubious, with stories of paid reviews being commonplace. Second, the opinion of a single person cannot be taken as a reflection of the preferences and tastes of the public. And this is the controversial aspect hinted at earlier. While a particular film may be of undoubtedly higher quality when compared to others, it does not make a conclusive case for it to attract a larger audience. This is not to say that the public would knowingly watch a disastrous film. The opinions of critics can be, for the lack of a better word, elitist, and not cater to more popular tastes. Additionally, sections of the public have been known to go for films for purely entertainment reasons, and are not generally bothered by subtle differences and details which are crucial for critics.

Hence, it is for this reason that viewer ratings have been chosen to stand in for film reviews as an explanatory variable. The number of persons rating films on IMDb.com varies from film to film, with each viewer giving a rating on a scale of 1 to 10, with the final score being the average of all ratings. With average rating being decided by hundreds, sometimes thousands of people [the minimum number of persons required for a film to be rated is five, leading to the exclusion of several films from our sample], it would not be too wrong to say that such an indicator would be more apt than a critic’s reviews.

Release Date:

The timing of a film’s release has become an extremely important factor in determining a film’s theatrical success, so much so that the previously sacred day of release, Friday, is not adhered to anymore. Public holidays and festivals are potentially the days which can attract the largest audiences, and film-makers try and time their releases so that they can take maximum advantage of the holidays, and an extended weekend, if available. However, there has been an added dimension to this aspect. Star actors like Salman Khan and Aamir Khan have made it a habit of releasing their films on a particular holiday every year [Eid and Christmas respectively], thus creating a niche week for themselves in the minds of the audience. This creates an expectation on part of the audience, and reduces promotional costs when it comes to enlightening the public regarding the film’s date of release. Additionally, by releasing his film on a holiday every year, an actor like Salman Khan makes going to his films an annual family/social

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affair; an event which the public looks forward to every year. This sort of curiosity and interest is extremely difficult to generate on an ordinary day of the year.

To understand the importance of the date of release to producers and stars alike, we could do worse than considering an interesting case which was brought to the Competition Commission of India (CCI). In mid-2012, Ajay Devgan Films Pvt. Ltd. had moved the CCI, alleging that Yash Raj Films was using its dominant position to ask exhibitors to dedicate more screens to its own upcoming release, Shahrukh Khan-starrer 'Jab Tak Hai Jaan', affecting Devgan's film 'Son of Sardaar'. While this might look like a dispute regarding collusion, what makes things interesting was the fact that the date of release of the two films in question, 'Jab Tak Hai Jaan' and 'Son of Sardaar', was the 13th of November –the Diwali week.

While the CCI rejected Devgn’s complaint, saying that ‘’the market cannot be restricted to any particular period like Eid or Diwali and the market has to be considered a market available throughout the year”, the practice of holiday releases beggars a second look.

It is for this reason that the date of release has been included as an explanatory variable. Release dates have been divided into three categories: holiday releases, seasonal releases, and normal releases. Holiday releases are those films which are released on particular holiday weeks: New Year’s Day/Eve, Republic Day, Valentine’s Day, Holi, Independence Day, Gandhi Jayanti, Eid-ul-Fitr, Dussehra, Diwali, Eid al-Adha [Bakrid], and Christmas/Christmas Eve. Seasonal releases are those films which have been releases in the summer months of May, June and July – the typical summer season months. A film released at any other time of the year is deemed to have been released on a normal day.

Number of cinemas released in:

Once the film-makers have identified their potential audience and targeted them with relevant information regarding their film, they have to make sure that their product is available as easily as possible, and that the audience is able to view the film as soon as it releases. Like any other product, the customer’s access to distribution channels can make or break a product. For a film to be a theatrical success, adequate distribution to cinemas on a national scale is paramount.

In the film industry, films can be distributed in three ways: wide release, platform release, and limited engagement. The last two methods are now obsolete, with wide release of films being the only way which producers use. Unless the film is a limited release due to the inability of the producer to finance the cost of making thousands of prints required for a wide release.

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The wide release of films results in a high initial cash flow, with producers hoping to cover the largest possible share of the audience in during the opening weekend through various means of awareness. Following the opening weekend, the producers hope for a positive reaction from the public and critics to keep the initial momentum going. Therefore, a film releasing in a large number of cinemas can be expected to earn a larger profit.

For our dependent variable, we take the theatrical revenues [net of taxes] earned by the films. While there are several ways to measure theatrical success - including net profits, total ticket sales, theatrical run in terms of attendance or days/weeks - total revenue is chosen for a simple reason: revenues are the most accessible and available statistic from those mentioned above.

It is important to keep in mind at this point that theatrical success is different for a producer. On a film’s gross revenues, tax has to be paid, which gives us our dependent variable, net revenues. From the remaining, distributors pocket their share, leaving producers with approximately 50-55% of the net revenues. However, there are several other revenue streams for a film which haven’t been accounted for in this study, but allow the producers to recoup their investments - and more - even if the film ‘’bombed’’ at the box-office. These include proceeds from the sale of the film’s TV rights, sale of related products and memorabilia, and DVD/BluRay sales. While the revenues from DVDs and HD prints have been falling for a while now due to online piracy/streaming, the terrestrial and satellite rights of films contribute a significant amount to the producers’ coffers. Unfortunately, accurate and reliable data isn’t readily available on these numbers, but if reports are to be believed, TV rights have been sold upwards of Rs.40Cr for some films. Additionally, as most TV deals are finalized before a film’s release, a negative response from the audience has no bearing on the payments.

What all this means is that theatrical success is important for the financiers of films; however, it’s not the be all and end all for them. The additional revenue streams mentioned above mean that a film which performs badly in theatres can still end up generating a profit for the producers. But the unavailability of data for these income sources prevents us from confirming the veracity of this statement.

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TABLE 1:

Film Variable Name Definition RangeDependent variable:

netrevenuesTheatrical revenues net of taxes (in crores of rupees)

0.01 – 202.57

Independent variables:Starpresence

dramad

horrord

romcomd

romanced

comedyd

actiondanimated

ud

uad

ad

reviewshold

sead

normal

cinemas

Where 1=star present in the film

Where 1=film’s genre is drama

Where 1=film’s genre is horror

Where 1=film’s genre is romantic comedy

Where 1=film’s genre is romantic

Where 1=film’s genre is comedy

Where 1=film’s genre is actionWhere 1=film’s genre is

animatedWhere 1=film has U CBFC

ratingWhere 1=film has UA CBFC

ratingWhere 1=film has A CBFC

ratingIMDb rating on 10

Where 1=film released on specified holidays

Where 1=film released in summer season

Where 1=film released on any other date

number of cinemas film opened in

Dummy variable

Dummy variable

Dummy variable

Dummy variable

Dummy variable

Dummy variable

Dummy variableDummy variable

Dummy variable

Dummy variable

Dummy variable

1.6 – 8.3Dummy variable

Dummy variable

Dummy variable

10 - 1526

IV. MODEL AND VARIABLE SPECIFICATION:

We first consider a linear model, but with a slight change to our dependeconsider the kernel density estimatesnormally distributed, and is in fact positively skewed to a great extent.

Figure 2: K-density Graph

Similar conclusions can be drawn from the normal probability plot of

Figure 3: Normal Probability Plot

Including netrevenues in our linear model as the dependent variable would compromise the OLS results we would obtain. While mean and variance of a normally distributed variable can be

0.0

2.0

4.0

6D

ensi

ty

0 50 100Net Revenues

MODEL AND VARIABLE SPECIFICATION:

, but with a slight change to our dependent variable. If we consider the kernel density estimates of netrevenues, we can see clearly that the data isn’t normally distributed, and is in fact positively skewed to a great extent.

Similar conclusions can be drawn from the normal probability plot of netrevenu

in our linear model as the dependent variable would compromise the While mean and variance of a normally distributed variable can be

150 200Net Revenues

22

nt variable. If we , we can see clearly that the data isn’t

netrevenu

in our linear model as the dependent variable would compromise the While mean and variance of a normally distributed variable can be

23

understood as the expectation of a future event and the risk associated with it, the same cannot be said for a variable which isn’t distributed normally. This is because the mean and variance of a variable with a heavy-tailed distribution like ours will be specific to the sample they have been obtained from, meaning that the mean and variance of different samples may be very different, even if they have been drawn from the same heavy-tailed distribution. To circumvent this problem, we instead take the natural logarithm of netrevenues.

To see if our transformation has had any effect, we plot the K-density graph for ln_netrev:

Figure 4

Our results are confirmed when we check the normal probability plots for ln_netrev:

Figure 5

Even though our transformed variable still isn’t distributed perfectly normally, we are now in a position to use ln_netrev as our dependent variable in our linear model.

0.0

5.1

.15

.2D

ens

ity

-5 0 5Ln_NetRev

0.00

0.25

0.50

0.75

1.00

Nor

mal

F[(

ln_n

etre

v-m

)/s]

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

24

After considering a linear model, we turn towards the more suitable – due to the distribution of netrevenues and ln_netrev – logit model. Our logit model will be of two types. The first will be a binary logit model, while the second will be an ordered logit model.

Binary Logit:

In our binary logit model, we transform our dependent variable, ln_netrev, into a dummy variable.

The above logistical regression will be executed with three different values of ln_netrev. These values are as follows:

i] x<-2 [variable name: rev_less_minus2]

ii] -2<x<+2.7 [variable name: rev_middle]

iii] x>+2.7 [variable name: rev_more27]

Ordered Logit:

For our ordered logit model, we create a new variable called rev_rank, whose construction will be explained at the appropriate stage.

These specific values to define our dummy variable have been derived from our kernel density graph shown in Figure 4. The values of ln_netrev of -2 and +2.7 refer to flat sections of the kernel density plot.

A film with an ln_netrev value of -2 has net revenue of Rs.0.135Cr, while a film with anln_netrev value of +2.7 has net revenue of Rs.14.88Cr.

We use the three models specified above – linear, binary logit and ordered logit – to analyse the performance of films in terms of the transformed variable ln_netrev and the possible explanatory variables.

25

V. EMPIRICAL FINDINGS:

To get started with our analysis of the data, we conduct a few simple correlation tests to check if our variables do indeed have any chance of explaining the variations in our dependent variables. The correlation matrices are as follows:

Tables 2-8:

ln_netrev starpresenceln_netrev 1.0000starpresence 0.4689 1.0000

ln_netrev holdln_netrev 1.0000hold 0.3342 1.0000

netrevenues week1netrevenues 1.0000week1 0.9669 1.0000

ln_netrev seadln_netrev 1.0000sead .0275 1.0000

ln_netrev cinemasln_netrev 1.0000cinemas .7390 1.0000

starpresence cinemasstarpresence 1.0000cinemas .6336 1.0000

reviews cinemasreviews 1.0000cinemas .0635 1.0000

While each of the correlation numbers has a story to tell, a few should be pointed out specifically. First, there is an extremely high degree of correlation between the week 1 revenues and net revenues in total. While this was expected, we can now say that high week 1 revenues and total revenues are co-incident. Second, comparing the correlation figures for ln_netrev v/s hold and those for ln_netrev v/s sead, we see that the former is correlated with

26

ln_netrev to a much higher degree than the latter, though the degree of correlation by itself is not very high.

The other correlation figures are as expected, with the interesting one being the correlation between reviews and cinemas. While it would not help to read too much into this at the initial stage, the correlation coefficient of .0635 indicates that highly rated films don’t always get screened in large number of cinemas.

We now examine the results of a simple linear model, with ln_netrev as our dependent variable.

Table 9:

Robustln_netrev Coef. Std. Err. t P>t [95% Conf. Interval]

cinemas .0052144 .0002951 17.67 0.000*** .0046337 .0057951reviews .2495741 .045409 5.50 0.000*** .1602207 .3389275ud -.1472041 .1863535 -0.79 0.430 -.5139007 .2194924uad -.1907349 .1689995 -1.13 0.260 -.5232831 .1418133ad (dropped)dramad -.2397574 .2990011 -0.80 0.423 -.8281159 .3486011horrord (dropped)romcomd .5647441 .3548767 1.59 0.113 -.1335633 1.263052romanced .1979865 .3321411 0.60 0.552 -.4555831 .851556comedyd -.0142536 .2998561 -0.05 0.962 -.6042945 .5757872actiond -.6329824 .3199871 -1.98 0.049** -1.262636 -.0033288animatedd -.4306682 .5584032 -0.77 0.441 -1.529464 .6681278hold -.3471715 .1895351 -1.83 0.068* -.7201286 .0257856sead (dropped)normald -.2876573 .1527018 -1.88 0.061* -.5881358 .0128211starpresence .613993 .1697671 3.62 0.000*** .2799343 .9480517_cons -2.481086 .3887217 -6.38 0.000 -3.245992 -1.716181Number of obs. = 320; F (13, 306) = 83.01; R-squared = 0.7886

From our OLS estimates it is clear that the significant variables are cinemas, reviews, starpresence and actiond, hold and normald. The signs for these variables are not as expected though, with hold and actiond having a negative sign, meaning that they have performed less well than their respective base categories.

Running the same regression again, but with a smaller number of independent variables, we get the following results:

27

Table 10:

Robustln_netrev Coef. Std. Err. t P>t [95% Conf. Interval]starpresence .6013391 .1684405 3.57 0.000*** .2698993 .9327789reviews .2533752 .0443286 5.72 0.000*** .16615 .3406003dramad -.3221797 .2992094 -1.08 0.282 -.9109328 .2665734horrord (dropped)romcomd .4804473 .3524455 1.36 0.174 -.2130583 1.173953romanced .1010931 .3228335 0.31 0.754 -.5341451 .7363312comedyd -.1095573 .3026069 -0.36 0.718 -.7049957 .485881actiond -.6955764 .3267478 -2.13 0.034 -1.338517 -.0526361animatedd -.5419662 .5377253 -1.01 0.314 -1.600046 .5161136hold -.0385655 .1504839 -0.26 0.798 -.3346721 .2575411sead .2852598 .1513286 1.89 0.060* -.012509 .5830285cinemas .0051865 .0002934 17.68 0.000*** .0046091 .0057639_cons -2.829904 .3622313 -7.81 0.000 -3.542665 -2.117143Number of obs. =320; F (11, 308) =98.83; R-squared= 0.7878

The story does not change much, with starpresence, reviews and cinemas continuing to be significant. However, sead is significant at 10% level, while hold and actiond cease to be significant. Additionally, there isn’t any improvement in the R2 value.

Removing the variable cinemas and running the above regression, we get:

Table 11:

Robustln_netrev Coef. Std. Err. t P>t [95% Conf. Interval]starpresence 2.594723 .2175696 11.93 0.000*** 2.166617 3.022828reviews .3031927 .0676316 4.48 0.000*** .1701159 .4362694dramad -1.540539 .4836285 -3.19 0.002*** -2.492161 -.5889175horrord (dropped)romcomd -.0224721 .5624149 -0.04 0.968 -1.129119 1.084175romanced -.60734 .5301849 -1.15 0.253 -1.650569 .4358894comedyd -.8854779 .4926923 -1.80 0.073* -1.854934 .0839783actiond -1.257294 .5409055 -2.32 0.021** -2.321618 -.1929701animatedd -2.102938 .9095132 -2.31 0.021** -3.892561 -.3133156hold -.0630277 .293926 -0.21 0.830 -.6413773 .5153219sead (dropped)normald -.6367393 .2314569 -2.75 0.006*** -1.09217 -.1813084_cons -.3901401 .5980673 -0.65 0.515 -1.56694 .7866595Number of obs=320; F (10, 309) = 33.75; R-squared= 0.4779

The removal of cinemas has led to a large drop in the value of R2 to .4779. However, the starpresence coefficient has increased, while at the same time, other variables like dramad,comedy, animated and actiond have become significant at varying levels of significance. To check the effect of cinemas, we construct a variable, cinemas_150, where the dummy variable

28

indicates that a film which opened in cinemas greater than 150 in number will receive a value of 1, otherwise 0. Replacing cinemas with cinemas_150, we get:

Table 12:

Robustln_netrev Coef. Std. Err. t P>t [95% Conf. Interval]starpresence 1.566407 .1699366 9.22 0.000*** 1.232023 1.900791reviews .1741821 .0483925 3.60 0.000*** .0789603 .2694038dramad -.3168753 .3785399 -0.84 0.403 -1.061727 .4279761horrord (dropped)romcomd .5744631 .43985 1.31 0.193 -.291028 1.439954romanced -.2630474 .4066574 -0.65 0.518 -1.063225 .5371308comedyd -.1985893 .3803807 -0.52 0.602 -.9470628 .5498842actiond -.378382 .4096186 -0.92 0.356 -1.184387 .4276228animatedd -.9071894 .6233631 -1.46 0.147 -2.133778 .3193996hold .0488441 .2153277 0.23 0.821 -.3748554 .4725436sead (dropped)normald -.5557725 .1706804 -3.26 0.001*** -.8916196 -.2199254cinemas_150 2.985381 .1884554 15.84 0.000*** 2.614558 3.356204_cons -2.406896 .4449512 -5.41 0.000 -3.282425 -1.531368Number of obs=320; F (11, 308) = 78.32; R-squared= 0.7317

Hence, the inclusion of cinemas, either in quantitative or qualitative form, improves our model significantly.

From our simple linear model, the following conclusions can be drawn:

The variables starpresence, reviews, and cinemas are all highly significant, with positive signs.

None of the age rating or genre categories are significant in either of the two regressions, except for actiond, which is significant at 5% in the first regression. This could mean that the identification of a successful genre or age group isn’t easy. If it was, then the unsuccessful categories would be avoided and the variety of films on offer would reduce sharply, along with the producers’ risks. However, this hasn’t happened, as we continue to see films of numerous genres being made and at the same time, producers not recovering their investments from theatrical proceeds.

The sead and normald variables are significant at 10% in the second and first regressions respectively.

Reading into the variables’ coefficients wouldn’t be too helpful as OLS models have the tendency to produce highly data specific parameters. With the data being highly heteroscedastic - and the rescaling of the dependent variable weakening the relevant tests - , it

29

makes sense to turn to a more pragmatic model so that the heavy-tailed net revenues distribution does not effect the results.

Binary Logit Models:

As mentioned previously, we will conduct three separate logistical regressions, using the modified dependent variables which have been defined earlier.

Case I: x<-2 [variable name: rev_less_minus2]

rev_less_minus2, where 1=ln_netrev<-2, 0 otherwise

Note: romcomd != 0 predicts failure perfectly, romcomd dropped and 20 obs not used

Note: romanced != 0 predicts failure perfectly, romanced dropped and 17 obs not used

Note: ud dropped due to collinearity

Note: animatedd dropped due to collinearity

Note: sead dropped due to collinearity

Logistic regression Number of obs =283

LR chi2 (11) =172.01

Prob > chi2=0.0000

Log likelihood = -37.955664 Pseudo R2=0.6938

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Table 13:

rev_less_m~2 Odds Ratio Std. Err. z P>z [95% Conf. Interval]starpresence .1355646 .1428667 -1.90 0.058* .0171828 1.069541reviews .7463113 .155484 -1.40 0.160 .4961163 1.122681uad .2692416 .2086238 -1.69 0.090* .0589634 1.229424ad .0833056 .0776364 -2.67 0.008*** .0134091 .5175468dramad .9821154 1.303613 -0.01 0.989 .0728313 13.24362horrord .6690868 2.420096 -0.11 0.912 .0005581 802.214comedyd 2.450966 3.359584 0.65 0.513 .1669475 35.98279actiond .8812746 1.408763 -0.08 0.937 .038408 20.2209hold .4240046 .4752899 -0.77 0.444 .0471208 3.815301normald .8144548 .5487725 -0.30 0.761 .2174377 3.050697cinemas .9588658 .0080748 -4.99 0.000*** .9431694 .9748234

Again, we see that the genre variables are not significant even at the 10% level. However, the other significant variables pan out as we expect them to. A film with a star is 1/7th as likely as a film without one to earn net revenues corresponding to ln_netrev<-2, i.e. Rs.0.135Cr. Similar conclusions can be drawn regarding the other significant variables.

If we take a look back at the correlations conducted initially, we see the high degree of correlation between cinemas and starpresence, which could mean that a film with a star present in it will be screened at a large number of cinemas. What could be happening here is that the cinemas variable might be capturing the effect of our star variable. To see if this is the case, we replace cinemas by cinemas_150. Running the same regression as above with cinemas_150 replacing cinemas, we see:


LR chi2 (11) =142.14

Prob > chi2=0.0000


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Table 14:

rev_less_m~2 Odds Ratio Std. Err. z P>z [95% Conf. Interval]starpresence .1909971 .1543108 -2.05 0.040** .0392033 .9305322reviews .8686142 .1438154 -0.85 0.395 .6279059 1.201598ud 5.524592 3.9089 2.42 0.016** 1.3805 22.10874uad 3.164553 2.011676 1.81 0.070* .910357 11.00051dramad 1.008992 1.505496 0.01 0.995 .0541786 18.79092comedyd 1.047347 1.634975 0.03 0.976 .0491259 22.32905actiond 1.725738 2.696313 0.35 0.727 .0807324 36.88944animatedd 2.028492 3.868587 0.37 0.711 .0482865 85.21596hold .9062971 .7803261 -0.11 0.909 .1676417 4.899583normald 1.577363 .9463339 0.76 0.447 .486694 5.112194cinemas_150 .0044416 .0046906 -5.13 0.000*** .0005606 .0351933

The likelihood of a star film earning less than Rs.0.135Cr has risen to 1/5th, while cinemas_150is significant at 1%. What is interesting to see here is that a film with a U rating is 5.5 times more likely than one with an A rating to earn less than Rs.0.135Cr, while a film with a UA rating is more than three times as likely. What clearly follows is that an adult film’s hype assures itself of some audience due to its bold themes, while a U or UA film cannot attract the audience as it does not stand out from the pool of available options.

Case II: -2<x<+2.7 [variable name: rev_middle]

rev_middle, where 1=-2<ln_netrev<2.7, 0 otherwise

To ensure that the effect of the star variable isn’t captured by cinemas, we will continue use cinemas_150.

Note: ad dropped due to collinearity

Note: horrord dropped due to collinearity

Note: hold dropped due to collinearity


LR chi2 (13) =70.45

Prob > chi2=0.0000


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Table 15:

rev_middle Odds Ratio Std. Err. z P>z [95% Conf. Interval]starpresence .1975353 .0666528 -4.81 0.000*** .1019602 .3827003reviews .8217385 .0774955 -2.08 0.037** .6830611 .9885706ud .5373632 .2167453 -1.54 0.124 .2437451 1.184677uad .5909278 .2276424 -1.37 0.172 .2777327 1.257308dramad 1.649782 1.323628 0.62 0.533 .342376 7.949681romcomd .4480027 .4114903 -0.87 0.382 .0740376 2.710871romanced 1.451965 1.412353 0.38 0.701 .2157631 9.770916comedyd 1.087016 .8681706 0.10 0.917 .227198 5.200771actiond 1.195125 .9757414 0.22 0.827 .2412475 5.920573animatedd 1.362973 1.481216 0.28 0.776 .1619724 11.46922sead 1.590628 .6385315 1.16 0.248 .7242154 3.493571normald 2.626958 .8890221 2.85 0.004*** 1.353279 5.099399cinemas_150 7.860663 2.954331 5.49 0.000*** 3.7631 16.41998

The interesting thing about the results shown above is that while the odds ratio of the star variable is approximately the same as in Table 12, the standard error of the variable in the second case is less than half of what it was in the first case. This means that the impact of a star is more certain in the second case than it is in the first.

Additionally, reviews too become significant with half the standard error, while the age rating variables become insignificant. Curiously, normald is significant at 1% level for films earning between Rs.0.135 and Rs.14.88Cr. However, this can be explained by the fact that films earning in this range are average to below average earners, and hence, their theatrical revenues aren’t bolstered by a large audience, which would exist for a holiday release. Therefore, films earning in this range are probably released on normal days of the year. Our hypothesis is verified when we see that more than 60% of the films falling under this category of net revenues were released on a normal day.

Cinemas_150 continues to be highly significant.

Case III: x>+2.7 [variable name: rev_more27]

rev_more27, where 1=ln_netrev>2.7, 0 otherwise

Casting a glance at the data, we can see that every film falling under this category was screened in more than 150 cinemas. We could either increase our threshold value of number of cinemas from 150 to a more appropriate figure, or we could use our old variable, cinemas. However, we shall continue with cinemas_150 to remain consistent.

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Note: animatedd != 0 predicts failure perfectly, animatedd dropped and 10 obs not used

Note: cinemas_150 != 1 predicts failure perfectly, cinemas_150 dropped and 77 obs not used

Note: ud dropped due to collinearity




LR chi2 (11) =95.51

Prob > chi2=0.0000


Table 16:

rev_more27 Odds Ratio Std. Err. z P>z [95% Conf. Interval]starpresence 14.26481 6.711943 5.65 0.000*** 5.672241 35.87378reviews 1.607103 .2426385 3.14 0.002*** 1.195447 2.160514uad .9564256 .3607195 -0.12 0.906 .4566856 2.003019ad .8727707 .5338885 -0.22 0.824 .2631515 2.894639dramad .5550423 .6452972 -0.51 0.613 .0568475 5.41927romcomd 2.791269 3.48518 0.82 0.411 .241541 32.25614romanced 1.486033 1.945744 0.30 0.762 .1141575 19.34428comedyd .8695191 .9819518 -0.12 0.901 .0950634 7.953254actiond .5040275 .5811179 -0.59 0.552 .0526093 4.828877hold 1.384437 .7099557 0.63 0.526 .5067186 3.782504normald .321616 .1412483 -2.58 0.010*** .1359891 .7606256

It’s rather clear that the impact of stars is the greatest for films whose net revenue falls under this category, i.e. greater than Rs.14.88Cr, with a film containing a star more than fourteentimes more likely compared to one without a star to earn net revenues exceeding Rs.14.88Cr. However, with such a high odds ratio comes a high standard error. This reflects reality to some extent, as a film containing a star isn’t assured of theatrical success. If we take a look at our data again, of the films which earned less than Rs.14.88Cr, 30% of them had star actors.

Reviews is also significant at the 1% level for the first time, indicating that our prejudice against Salman Khan’s blockbuster - but senseless films - is unfounded to an extent. An increase in one review rating makes it 1.6 times more likely that a film will earn more than Rs.14.88Cr.

Continuing with the prevailing trend, none of the age certificate dummies or the genre dummies are significant.

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However, the normald variable is significant, and again, it confirms our theory, with a film releasing on a normal day .3 times less likely than a film releasing in the summer season to earn more than Rs.14.88Cr. Sadly, our holiday variable is rather insignificant, though it makes interesting viewing that the odds ratio for hold is 1.3.

Ordered Logit Model:

In our ordered logit model, our dependent variable is rev_rank from which we are going to see what relationships exist with our independent variables. Rev_rank is a function of our original dependent variable ln_netrev that is not measured here. The continuous latent variable ln_netrev has various threshold points. The value of the observed variable rev_rank depends on whether or not we have crossed a particular threshold on ln_netrev. Our dependent variable, rev_rank, is going to be treated as ordinal under the assumption that the levels of rev_rankhave a natural ordering (low to high), but the distances between adjacent levels are unknown.The results are as follows:

Note: ad dropped due to collinearity



Ordered logistic regression Number of obs=320

LR chi2 (13) =270.69

Prob > chi2=0.0000


Table 17:

rev_rank Coef. Std. Err. z P>z [95% Conf. Interval]starpresence 2.500437 .3990939 6.27 0.000*** 1.718228 3.282647

reviews .2822319 .1011452 2.79 0.005*** .083991 .4804729ud -.8693556 .4262817 -2.04 0.041** -1.704852 -.0338587

uad -.6572204 .4041728 -1.63 0.104 -1.449384 .1349437dramad -.1896268 .8424434 -0.23 0.822 -1.840786 1.461532

romcomd 1.427411 .9863616 1.45 0.148 -.5058216 3.360645romanced .9892877 1.016332 0.97 0.330 -1.002687 2.981262comedyd -.0111722 .8435661 -0.01 0.989 -1.664531 1.642187actiond -.5773632 .8654223 -0.67 0.505 -2.27356 1.118833

animatedd -1.726503 1.482306 -1.16 0.244 -4.63177 1.178764sead -.3393796 .4203552 -0.81 0.419 -1.163261 .4845015

normald -1.187817 .3556009 -3.34 0.001*** -1.884782 -.4908519cinemas_150 5.411791 1.046846 5.17 0.000*** 3.36001 7.463572

/cut1 .2136461 .9163079 -1.582284 2.009577/cut2 7.894096 1.416365 5.118073 10.67012

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The positive coefficients for starpresence, reviews and cinemas_150 mean that the likelihood of net revenue increasing does increase with an increase in the aforementioned factors.Similarly, the negative coefficient for ud and normald imply that a decrease in the value of these two variables increases the likelihood of net revenues increasing. This fits with our thought process, as a decrease in the value of ud is an indication to the audience that the film is not a childish film, and hence, it attracts the large adult population. A negative coefficient for normald is also expected as holiday being the reference category, the likelihood of a film’s net revenues rising increase if the film is released in a holiday week.

The threshold parameters of .2136461 and 7.894096 tell us the following. Cut1 is the estimated threshold point on the latent variable [ln_netrev] used to differentiate low rev_rank from middle and high rev_rank when values of the predictor variables are evaluated at zero. Filmsthat had a value of ln_netrev of .2136461[corresponding to approximately Rs.1.24Cr] or less would be classified as low rev_rank, given they had no stars, were assigned an A age certificate, were released in a holiday week, and were of the horror genre, along with a 0 film rating. The same applies for cut2. However, the value of cut2 here is rather high. Coupled with a large standard error, it means that we should run another regression, with some variables excluded from the model.

Considering what we have learnt so far, it would not be too wrong to exclude the genre dummies and the age certificate dummies from our model. Additionally, the presence ofcinemas or cinemas_150 variable in the model along with the star variable could cause multicollinearity issues. Hence, we exclude these variables and strip our model down to the bones. The results we get are as follows:


Ordered logistic regression Number of obs=320

LR chi2 (4) =139.00

Prob > chi2=0.0000


Table 18:

rev_rank Coef. Std. Err. z P>z [95% Conf. Interval]starpresence 2.867069 .3529176 8.12 0.000*** 2.175363 3.558775

reviews .2686564 .0831633 3.23 0.001*** .1056592 .4316535sead -.2387712 .3904486 -0.61 0.541 -1.004036 .5264941

normald -.8917512 .3284885 -2.71 0.007*** -1.535577 -.2479256/cut1 -.4859686 .5321801 -1.529023 .5570853/cut2 3.746816 .6204426 2.530771 4.962861

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What we now have is a model which validates our theory. Starpresence and reviews both have positive signs on their coeffiecients. While sead is not significant, normald is highly significant, and has a negative coefficient, implying that a reduction in the value of normaldto 0 – which would refer to the reference category, the holiday dummy – would increase the likelihood of a film’s net revenues rising.

The threshold parameters of -.4859686 and 3.746816 correspond to net revenues of Rs.0.62Cr and Rs.42.39Cr, which fits our data well.

In addition to the models constructed in this study, a simultaneous-quantile regression model can also shed some light on the relationships we have been examining. The results are summarized below.

[Note: the dependent variable is netrevenues. Signs in parenthesis indicate the sign of the coefficients. Appearance of a variable in a particular coloumn is indicative of the level of significance it is significant at.]

Table 19:

1% 5% 10%10 cinemas[+], normald[-]20 cinemas[+], normald[-] reviews[+] romcomd[+]30 cinemas[+], reviews[+], normald[-]40 cinemas[+], reviews[+], normald[-]50 cinemas[+], reviews[+] normald[-] starpresence[+],

romcomd[+]60 cinemas[+] reviews[+] romcomd[+], normald[-]70 cinemas[+] reviews[+],romcomd[+]80 cinemas[+] reviews[+]90 cinemas[+] reviews[+]99 cinemas[+], reviews[+] romcomd[+], hold[+] romanced[-], comedyd[+]

[Refer to Appendix for complete results]

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VI. CONCLUDING REMARKS:

While the linear regression model does provide useful insights into the relationships between our chosen variables, the OLS estimates are undermined due to the non-normal distribution followed by net revenues. This issue can be partially overcome by transforming net revenues by taking their natural logarithm. However, a better proposition would be to use a logistical approach.

Using the above approaches it can be concluded that while the number of cinemas, reviews and release date are significant factors, none of the genre or age certificate categories were consistently significant. The simultaneous quantile regression model provides another path to examine the influence of factors on net revenues.

As always, data limitations mean that certain variables cannot be included in the models, while some observations have to be excluded from the sample due to incomplete data. While this might reduce the efficiency and strength of the models, the basic framework available provides ample clues regarding the influence of the factors considered.

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APPENDIX:

Simultaneous Quantile Regression

Note: animatedd dropped due to collinearity


Simultaneous quantile regression Number of obs=320 bootstrap (20) SEs .10 Pseudo R2=0.1766

.20 Pseudo R2=0.2334

.30 Pseudo R2=0.2899

.40 Pseudo R2=0.3489

.50 Pseudo R2=0.4089

.60 Pseudo R2=0.4518

.70 Pseudo R2 =0.4932

.80 Pseudo R2 =0.5104

.90 Pseudo R2 = 0.5392

.99 Pseudo R2 =0.6842

Tables 20-29:

Bootstrapnetrevenues Coef. Std. Err. t P>t [95% Conf. Interval]

q10starpresence -.0347375 .4027679 -0.09 0.931 -.8272623 .7577874

reviews .2439106 .1715401 1.42 0.156 -.0936281 .5814493dramad .6338434 .9273783 0.68 0.495 -1.190955 2.458642horrord .3509274 1.767999 0.20 0.843 -3.127957 3.829811

romcomd 2.437564 2.141349 1.14 0.256 -1.77596 6.651089romanced -.390894 1.143627 -0.34 0.733 -2.641204 1.859416comedyd .3522234 .9752719 0.36 0.718 -1.566815 2.271262actiond -.3490614 1.276949 -0.27 0.785 -2.861709 2.163586

hold -.1085698 .677123 -0.16 0.873 -1.440942 1.223802normald -1.46495 .5538282 -2.65 0.009*** -2.554715 -.3751842cinemas .0140349 .0030934 4.54 0.000*** .007948 .0201217

_cons -3.896045 2.23151 -1.75 0.082 -8.286978 .4948892

39

q20starpresence .1007096 .8260273 0.12 0.903 -1.524661 1.72608

reviews .5198581 .216142 2.41 0.017** .0945563 .9451599dramad 1.403972 1.585575 0.89 0.377 -1.715959 4.523902horrord .7656737 1.646176 0.47 0.642 -2.4735 4.004847

romcomd 4.371205 2.340164 1.87 0.063* -.2335262 8.975936romanced -.2130498 2.34919 -0.09 0.928 -4.835542 4.409442comedyd 1.154468 1.697319 0.68 0.497 -2.18534 4.494276actiond -.2731208 1.627989 -0.17 0.867 -3.476509 2.930267

hold .5307091 1.082354 0.49 0.624 -1.599034 2.660453normald -1.758156 .5717402 -3.08 0.002*** -2.883167 -.6331449cinemas .0178043 .0036214 4.92 0.000*** .0106784 .0249301

_cons -6.38078 2.981597 -2.14 0.033 -12.24766 -.5139036

q30starpresence -.3553014 1.240781 -0.29 0.775 -2.796781 2.086178

reviews 1.093373 .3293384 3.32 0.001*** .4453357 1.741411dramad 2.659639 2.895459 0.92 0.359 -3.037745 8.357022horrord .3883732 3.468709 0.11 0.911 -6.436992 7.213738

romcomd 5.09006 4.626767 1.10 0.272 -4.01401 14.19413romanced 2.109277 2.969764 0.71 0.478 -3.734315 7.952869comedyd 2.334096 2.900478 0.80 0.422 -3.373162 8.041355actiond 1.017294 3.01068 0.34 0.736 -4.906809 6.941397

hold 1.166446 1.692551 0.69 0.491 -2.163979 4.496871normald -2.790904 .8576428 -3.25 0.001*** -4.478484 -1.103323cinemas .0279663 .003945 7.09 0.000*** .0202036 .0357289

_cons -11.97584 4.304921 -2.78 0.006 -20.44662 -3.505068

q40starpresence 1.587283 1.380502 1.15 0.251 -1.129126 4.303692

reviews .9359159 .3147148 2.97 0.003*** .3166528 1.555179dramad -.7419284 3.018447 -0.25 0.806 -6.681315 5.197458horrord -3.663068 4.087722 -0.90 0.371 -11.70646 4.380326

romcomd 8.42477 5.472431 1.54 0.125 -2.343312 19.19285romanced -2.053041 3.329616 -0.62 0.538 -8.604713 4.498631comedyd -1.170027 3.017857 -0.39 0.699 -7.108253 4.768198actiond -3.495448 4.093788 -0.85 0.394 -11.55078 4.559883

hold .7546488 2.16298 0.35 0.727 -3.501439 5.010736normald -2.010028 .6741462 -2.98 0.003*** -3.336542 -.6835129cinemas .0289876 .0026108 11.10 0.000*** .0238503 .0341249

_cons -7.463285 4.433994 -1.68 0.093 -16.18804 1.261468

40

q50starpresence 2.177344 1.169891 1.86 0.064* -.1246453 4.479333

reviews .9363589 .2963748 3.16 0.002*** .3531835 1.519534dramad -.3745437 2.876154 -0.13 0.896 -6.033941 5.284854horrord -2.424845 4.320236 -0.56 0.575 -10.92576 6.076065

romcomd 9.610061 5.633541 1.71 0.089* -1.475034 20.69516romanced -1.858111 3.10779 -0.60 0.550 -7.973298 4.257076comedyd -.8985633 2.970755 -0.30 0.762 -6.744105 4.946979actiond -3.440597 3.670793 -0.94 0.349 -10.6636 3.782406

hold .7133407 2.481289 0.29 0.774 -4.169082 5.595764normald -1.976631 .8294242 -2.38 0.018** -3.608686 -.3445765cinemas .031246 .0028143 11.10 0.000*** .0257084 .0367836

_cons -7.781814 3.861339 -2.02 0.045 -15.37976 -.1838722

q60starpresence 1.288312 1.212317 1.06 0.289 -1.097159 3.673784

reviews .8745285 .4014538 2.18 0.030** .0845895 1.664468dramad -.6815139 4.054652 -0.17 0.867 -8.659836 7.296808horrord -4.119565 5.110822 -0.81 0.421 -14.17611 5.936979

romcomd 8.268183 5.001179 1.65 0.099* -1.572617 18.10898romanced -3.623612 4.379346 -0.83 0.409 -12.24083 4.99361comedyd -1.283668 4.223106 -0.30 0.761 -9.593456 7.02612actiond -3.836717 4.572487 -0.84 0.402 -12.83398 5.160546

hold .5467856 3.551185 0.15 0.878 -6.440866 7.534437normald -2.187966 1.169523 -1.87 0.062* -4.489231 .113299cinemas .0351907 .0018488 19.03 0.000*** .0315528 .0388286

_cons -6.552791 5.026418 -1.30 0.193 -16.44325 3.337671

q70starpresence .9601587 1.807443 0.53 0.596 -2.596339 4.516656

reviews .9023099 .4439799 2.03 0.043** .0286924 1.775927dramad 1.200448 2.855882 0.42 0.675 -4.419061 6.819956horrord -1.97856 4.150045 -0.48 0.634 -10.14459 6.187467

romcomd 11.68691 5.513779 2.12 0.035** .8374681 22.53635romanced -3.926863 3.563743 -1.10 0.271 -10.93923 3.0855comedyd .5345937 3.153564 0.17 0.865 -5.670661 6.739848actiond -.0571301 3.689809 -0.02 0.988 -7.317552 7.203292

hold 4.726373 3.531509 1.34 0.182 -2.222563 11.67531normald -1.629705 1.14305 -1.43 0.155 -3.878881 .6194706cinemas .0390058 .002644 14.75 0.000*** .0338032 .0442084

_cons -8.305002 3.849542 -2.16 0.032 -15.87973 -.7302724

41

q80starpresence 2.949565 2.971157 0.99 0.322 -2.89677 8.795899

reviews .8445002 .4863345 1.74 0.083* -.1124583 1.801459dramad .4626165 1.672251 0.28 0.782 -2.827866 3.753099horrord -4.448834 4.575747 -0.97 0.332 -13.45251 4.554846

romcomd 14.29516 10.43163 1.37 0.172 -6.231103 34.82143romanced -4.0514 2.554069 -1.59 0.114 -9.077032 .9742315comedyd -.43225 2.177557 -0.20 0.843 -4.717021 3.852521actiond .6459168 3.839675 0.17 0.867 -6.909396 8.20123

hold 4.979978 5.598646 0.89 0.374 -6.036455 15.99641normald -.3226166 1.806538 -0.18 0.858 -3.877335 3.232102cinemas .0407433 .0054984 7.41 0.000*** .0299242 .0515625

_cons -7.278251 3.691775 -1.97 0.050 -14.54254 -.0139609

q90starpresence -.2445296 4.669079 -0.05 0.958 -9.431859 8.942799

reviews 1.110121 .6304674 1.76 0.079* -.1304475 2.350689dramad .1892321 1.713388 0.11 0.912 -3.182194 3.560658horrord -3.516134 6.245969 -0.56 0.574 -15.8063 8.774034

romcomd 15.39659 11.34589 1.36 0.176 -6.928665 37.72184romanced -5.703029 3.526869 -1.62 0.107 -12.64283 1.236777comedyd -1.96101 2.945805 -0.67 0.506 -7.757459 3.83544actiond 1.250628 2.645654 0.47 0.637 -3.955214 6.45647

hold 3.471775 16.91862 0.21 0.838 -29.81893 36.76248normald 1.277224 2.548212 0.50 0.617 -3.736882 6.291331cinemas .0647189 .0111089 5.83 0.000*** .04286 .0865779

_cons -9.610738 4.352527 -2.21 0.028 -18.17519 -1.046288

q99starpresence -5.447936 5.52564 -0.99 0.325 -16.32071 5.424843

reviews 3.439377 1.301276 2.64 0.009*** .8788607 5.999893dramad 6.830157 4.911611 1.39 0.165 -2.8344 16.49471horrord 11.21147 8.151725 1.38 0.170 -4.828649 27.25158

romcomd 22.63105 10.57424 2.14 0.033** 1.824166 43.43794romanced -7.451973 4.454945 -1.67 0.095* -16.21795 1.314004comedyd 12.03302 6.360322 1.89 0.059* -.4821638 24.5482actiond 5.115931 7.369442 0.69 0.488 -9.38489 19.61675

hold 109.965 44.49814 2.47 0.014** 22.40624 197.5239normald -.9918132 3.22956 -0.31 0.759 -7.346605 5.362978cinemas .0654622 .0090686 7.22 0.000*** .047618 .0833064

_cons -17.74303 5.512516 -3.22 0.001 -28.58999 -6.896074

42

NOTES:

1. Economic Contribution of Indian Film & Television Industry, March 2010 [prepared by PWC for MPDA].

2. Gulzar; Nihalan, Govind; Chatterjee,Saibal (2003). Encyclopedia of Hindi Cinema.

3. Wallace, W. T., Seigerman, A., & Holbrook, M. B. (1993). “The Role of Actors andActresses in the Success of Films: How Much is a Movie Star Worth?” Journal of Cultural Economics, 17(1), 1-27.

4. De Vany A, Walls WD. (1999)“Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box-Office?’’Journal of Cultural Economics, 23, 285–318.

5. Einav, Liran (2007) “Seasonality in the U.S. motion picture industry.” Journal of Economics, 38(1), 127-145.

6. Holbrook, Morris B., &Michela Addis (2007). “Taste versus the Market: An Extension of Research on the Consumption of Popular Culture.” Journal of Consumer Research,24(3), 415-24.

7. Gemser, G., Van Oostrum, M., &Leenders, M. (2007). “The Impact of Film Reviews on the bBox Office Performance of Art House Versus Mainstream Motion Pictures.” Journal of Cultural Economics, 31(1), 43-63.

8. Deuchert, Eva, Adjamah, Kossi, &Pauly, Florian (2005). “For Oscar Glory or Oscar Money? Academy Awards and Movie Success.” Journal of Cultural Economics, 29, 159-176.

9. Elberse, Anita and Bharat Anand (2007) “The Effectiveness of Pre-release Advertising for Motion Pictures: An Empirical Investigation using a Simulator Market.”Information Economics and Policy, 19, 319-43.

10. Smith SP, Smith VK. (1986). “Successful Movies: a Preliminary Empirical Analysis.”Applied Economics, 18, 501–507.

11. Prag J, Casavant J. (1994). “An Empirical Study of the Determinants of Revenues and Marketing Expenditures in the Motion Picture Industry.” Journal of Cultural Economics, 18, 217–227.

12. Litman, Barry R. & Kohl, Linda S. (1989). “Predicting Financial Success of Motion Pictures: The ‘80s Experience.” Journal of Media Economics, 2, 35-50.

43

13. De Vany A, Walls WD. (1996). “Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry.” Economic Journal, 106, 1493–1514.

De Vany A, Walls WD. (1999). “Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box-Office?’’Journal of Cultural Economics, 23, 285–318.

De Vany A, Walls WD. (2001). “Motion Picture Profit, the Stable Paretian Hypothesis and the Curse of the Superstar.” Working Paper, Institute for Mathematical Behavioural Science, University of California, Irvine.

De Vany A, Walls WD. (2002). “Does Hollywood Make Too Many R-rated Movies? Risk, Stochastic Dominance and the Illusion of Expectation.” Journal of Business.

14. De Vany A, Lee CHK. (2001). “Quality Signals in Information Cascades and the Distribution of Motion Picture Box Office Revenues.”Journal of Economic Dynamics and Control, 25(3–4), 593–614.

15. Collins, Alan, Hand, Chris, & Snell, Martin C. (2002). “What Makes a Blockbuster? Economic Analysis of Film Success in the United Kingdom.” Managerial and Decision Economics, 23, 343-354.

16. The study conducted by a few students of IIM-A amounted to finding out if there was any correlation between advertising outlay and film revenues. However, the study was riddled with errors, starting with a selection bias, along with the final conclusion that a high advertising budget meant greater revenues – in effect, saying that correlation implies causation. Unfortunately, the study isn’t available in the public domain for a more careful examination.

17. Austin B. (1980). “Rating the Movies.” Journal of Popular Film and Television, 7(4), 384–399.

44

References:

Austin B. (1980). “Rating the Movies.” Journal of Popular Film and Television, 7(4), 384–399.

Box Office India (2013). <http://boxofficeindia.com/>

Collins, Alan, Hand, Chris, & Snell, Martin C. (2002). “What Makes a Blockbuster? Economic Analysis of Film Success in the United Kingdom.” Managerial and Decision Economics, 23, 343-354.

De Vany A, Lee CHK. (2001). “Quality Signals in Information Cascades and the Distribution of Motion Picture Box Office Revenues.” Journal of Economic Dynamics and Control, 25(3–4), 593–614.

De Vany A, Walls WD. (1996). “Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry.” Economic Journal, 106, 1493–1514.

De Vany A, Walls WD. (1999). “Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box-Office?’’Journal of Cultural Economics, 23, 285–318.

De Vany A, Walls WD. (2001). “Motion Picture Profit, the Stable Paretian Hypothesis and the Curse of the Superstar.” Working Paper, Institute for Mathematical Behavioural Science, University of California, Irvine.

De Vany A, Walls WD. (2002). “Does Hollywood Make Too Many R-rated Movies? Risk, Stochastic Dominance and the Illusion of Expectation.” Journal of Business.

Deuchert, Eva, Adjamah, Kossi, &Pauly, Florian (2005). “For Oscar Glory or Oscar Money? Academy Awards and Movie Success.” Journal of Cultural Economics, 29, 159-176.

Economic Contribution of Indian Film & Television Industry, March 2010 [prepared by PWC for MPDA].

Einav, Liran (2007) “Seasonality in the U.S. motion picture industry.” Journal of Economics, 38(1), 127-145.

Elberse, Anita and Bharat Anand (2007) “The Effectiveness of Pre-release Advertising for Motion Pictures: An Empirical Investigation using a Simulator Market.”Information Economics and Policy, 19, 319-43.

Gemser, G., Van Oostrum, M., &Leenders, M. (2007). “The Impact of Film Reviews on the bBox Office Performance of Art House Versus Mainstream Motion Pictures.” Journal of Cultural Economics, 31(1), 43-63.

45

Gulzar; Nihalan, Govind; Chatterjee,Saibal (2003). Encyclopedia of Hindi Cinema.

Holbrook, Morris B., &Michela Addis (2007). “Taste versus the Market: An Extension of Research on the Consumption of Popular Culture.” Journal of Consumer Research,24(3), 415-24.

Internet Movie Database (2013). <http://imdb.com/>

Litman, Barry R. & Kohl, Linda S. (1989). “Predicting Financial Success of Motion Pictures: The ‘80s Experience.” Journal of Media Economics, 2, 35-50.

Litman B. (1983). “Predicting Success of Theatrical Movies: An Empirical Study.” Journal of Popular Culture, 16, 159–175.

Prag J, Casavant J. (1994). “An Empirical Study of the Determinants of Revenues and Marketing Expenditures in the Motion Picture Industry.” Journal of Cultural Economics, 18, 217–227.

Smith SP, Smith VK. (1986). “Successful Movies: a Preliminary Empirical Analysis.”Applied Economics, 18, 501–507.

Wallace, W. T., Seigerman, A., & Holbrook, M. B. (1993). “The Role of Actors andActresses in the Success of Films: How Much is a Movie Star Worth?” Journal of CulturalEconomics, 17(1), 1-27.

Walls, W. D. (2009). “Screen Wars, Star Wars, and Sequels: Nonparametric Reanalysis of Movie Profitability.” Empirical Economics, 37(2), 447-461.

Documents

Master's Thesis - Factors Affecting Theatrical Success of Films: An Empirical Review