Domestic Box Office Success (1980-2016) I. Introduction The movie industry has been developing for hundreds of years, especially from the late 19 th century until what it is today in the 21 st century. It is a pivotal industry in entertainment today, which has brought enjoyment for many people. There are hundreds of films being released each year, which greatly differ in successes. From this knowledge we decided to research more into what makes a film more or less successful. The purpose is to determine a film’s success in U.S. domestic markets and to find various data that would determine if certain variables will or will not make a movie more or less successful. We will estimate an Ordinary Least Squares regression to attempt to explain the monetary success based off of variables such as budget, genre, rating, etc. Will the factors that we see such as rating, genre or MPAA affect the success of a film relative to another? The foundation of the research project is based off of this question, where it will be expanded the further we move forward with this project.
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Domestic Box Office Success (1980-2016)
I. Introduction
The movie industry has been developing for hundreds of years, especially from the late
19th century until what it is today in the 21st century. It is a pivotal industry in entertainment
today, which has brought enjoyment for many people. There are hundreds of films being
released each year, which greatly differ in successes. From this knowledge we decided to
research more into what makes a film more or less successful. The purpose is to determine
a film’s success in U.S. domestic markets and to find various data that would determine if
certain variables will or will not make a movie more or less successful. We will estimate an
Ordinary Least Squares regression to attempt to explain the monetary success based off of
variables such as budget, genre, rating, etc. Will the factors that we see such as rating,
genre or MPAA affect the success of a film relative to another? The foundation of the
research project is based off of this question, where it will be expanded the further we
move forward with this project.
II. Literature Review
Although our project was almost entirely created from intuition and self-knowledge of
what we know of the movie industry we decided to find out if there were any other tests by
other economists to determine the success in the film industry. From that, we found a few
articles and research papers, but we will be using our basis off of a paper that attempts to
answer nearly the same question, called “Examining Success in the Motion Picture Industry”
by Pat Topf. Topf has decided to use Total revenue as the basis for the project, with the
variables of Production Costs, Star Power, Age-Appropriate rating as a dummy variable,
Genre, Sequel, Summer/Winter Release, Holiday Release, as well as an interaction variable
between advertising costs and professional review scores. From this research we have
decided to use it as our basis and how our model will differ from Topf’s model as we will be
using a few different variables, while trying to achieve the same result in determining the
success of a film in the motion picture industry.
III. Theoretical Model
We have decided to adjust all of the data for ticket price inflation to give a more
accurate representation of the impact of the movie in the box office through numerous
variables. The dependent variable is Domestic Gross adjusted in 2016 dollars, which we will
be determine if it is affected by: the production budget, the maximum number of theaters
that it is released in, the maximum number of weeks that it was released in a theater,
whether or not it is a sequel or not, the IMDB rating, Rotten Tomatoes Rating, the Motion
Picture Association of America (MPAA) film rating system of G, PG, PG-13, R and Unrated.
As well as the quarter of the year that it was released in, as well as all of the genres that it is
considered from Box Office Mojo.
IV. Data
The dataset that we analyzed is the top 102 films in box office gross, released in the
United States from the year 1980 to 2016. The data was chosen to be after the year 1980 as
we mainly looked pre-1980, as we felt that the production budget has one of the largest
impacts on the domestic gross of the movies. The choice of data, ensures accuracy for our
research, as it would give us complete data for the entirety of our sample size. We have
decided to use the top 102 grossing movies of all time from the given time frame as our
sample size, where it would not be an accurate representation of any movie being released
to make a guaranteed x amount from certain variables, but rather the most successful
movies have certain variables in them that would or would not have made them more or
less successful. All of the data will be available from three websites that we have chosen
that are considered one of the most popular data websites in the movie industry. We will
be using the Internet Movie Database, Rotten Tomatoes, and Box Office Mojo.
For our dependent model we will be determining the domestic gross of a film, in
millions of dollars. This variable is adjusted for inflation and will be represented as
(Domestic Gross). It is the revenue that a film has grossed during its theatrical release.
For our first independent variable, we have felt that this is our most important variable
which is the production budget, which will be denoted as (Production), as it is the amount
of money that the directors and producers of a film is given in order to produce a film by
their production company. For our second variable, we have decided to use (Theaters),
which will be the maximum number of theatres that the movie was widely released in
during its theatrical release. While (Weeks) will be the number of weeks that the given
movie in our population was in theaters at any given time. We have decided to use the
ranking from the Internet Movie Database from a scale of 1-10 based off of the average
review by an IMDB user, which is going to represented as (IMDB), which we will do the
same for Rotten Tomatoes critics, but instead being rated from a scale of 1-100, which will
be denoted as (RoT).
We have quite a few dummy variables in our data, which will be represented by a 1 if it
fulfills the category, and 0 if otherwise not fulfilling the specific category. For these dummy
variables we will be using (Release), which determines whether or not the movie was re-
released in theatre at any time, which we believe is a pivotal factor in how much revenue
that a movie can potentially earn. As well as (Sequel), which may give some movies an edge
in the market as it is part of a film in a series. We will only be using this variable if it is truly
a sequel to a previous film. We will also be using a few dummy variables, which will be the
rating that the Motion Picture Association of America gives a film, also known as the MPAA
rating which scales from G to NC-17, while we chose to remove NC-17 and Unrated as we
have found that no films in our sample size were considered either of those. The last
dummy variable that we will be using is which quarter that the movie was released in. We
originally wanted to measure whether or not it was released in the summer or winter
months as we felt that it would be a good indicator (Topf, 2009). But, we have decided not
to as many movies were not released in those specific seasons, but rather two or three
weeks before, so we have decided to just opt with using the quarter system as many
companies do quarterly releases on their financial statements.
V. Empirical Model
For our project we have decided to run a few models making adjustments as needed in
order to try to find the best feasible model given the data that was readily available for us.
After much discussion as a group we have decided to come up with two models that will be
analyzed, with our preliminary model involving all of the independent variables being
previously mention. While on the other had our Adjusted model omitting the variables:
Sequel, IMDB Rating, as well as Rotten Tomatoes Rating.