movies and market share
TRANSCRIPT
Movies and Market Share
I. Introduction:
Watching movies has always been Americans’ favorite pastime activity. Every year,
hundreds of movies were produced and released in America, averaging 14 movies per
week. However, according to Britain’s Screen Digest, Americans only saw an average of
3.88 movies in 2013, ranked 2nd place, behind Koreans. [1] Last year, even though already
on decline, the movie industry still managed to sell 1.34 billion tickets, for a 10.90 billion
in revenue. [2] On top of that, Hollywood itself supported more than 6,000 businesses, and
employed more than 62,000 workers. [3] Most of the movies are action and adventure
(46.7%), second are comedy films (21.9%), thrillers stand in the 3rd place with 14.5%,
dramas and other types took the rest. [4]
As a rather special industry, the movie and video producing industry differs from
others in a lot of way. First, the industry is highly concentrated with big players and, as a
result, there is a lot of competition between them. The top seven corporations took with
them 74.2% market share, with 21st Century Fox being the biggest. Second, unlike other
industries, producing movies doesn’t require as much materials or expensive equipment,
as the percentage of purchasing input is only half that of other industries. Even more
surprising, the percentage of wages in total cost is even slight less than average. This could
be explained by the fact that while a movie was done by so many people from tailors to
sound editors, only a selected few actors, actresses and/or directors are paid handsomely.
On the other hands, most of the movie producing cost went to marketing and rent, as both
of them combined captured 35% of total cost. As we introduced earlier, fierce competition
must be a driving force behind the huge marketing cost. [5]
Given their competitive nature, it is natural that most of their movies are equipped
with famous actors/actresses, talented directors, and huge budgets. Using window-sliding
logit model, we would try to determine if any of those characteristics would help increase
a movie earning. As this is a popular topic, there has been quite a few studies on predicting
movie success based on known characteristics. Most notably of those is the paper Modeling
Movie Life Cycle And Market Share by Ainslie, Dreze and Zufryden, whose work we
would base our own study on. In their study, Ainslie et al (2005) used the logit model to
estimate the effect of several characteristics like number of opening weeks, MPAA ratings,
genre and, most importantly, actors and actresses on a movie’s revenue. In our study, we
would expand the scope even further, studying more than 700 movies over the span of 3
years. We also look at more variables, namely the name of distributor, budgets, runtime,
etc. On top of that, we also look at movie ratings using data from popular review sites since
the convenience of mobile devices allowed the consumers to access more data than ever
before, and thus those review sites are increasingly playing a larger role in shaping public
opinion than when Ainslie et al did their study.
II. Empirical Analysis
a. Data:
In order to accomplish our analysis, we gathered data on several different variables on the
selection of movies for the time period that was chosen. Our data pertains to four time periods,
movies released on the years ranging from 2010 to 2013. For each movie we gathered data on 27
different variables, the day the movie was released, the box office rank it obtained for the year, the
day the movie leave the theaters, the MPAA rating, gross revenue, the total amount to tickets sold,
the critique's score from Rotten Tomatoes, the Audience score from Rotten Tomatoes, the Certified
Fresh award that is given by Rotten Tomatoes, the total runtime, budget, number of days in theater,
the maximum number of theaters that were airing the movie at a given time, the domestic revenue,
the weekly sales from weeks one through ten, and the season the movie was released on. In order
to be able to parameterize the qualitative variables, Distributor, Genre, MPAA rating, and Season,
we set a numerical value for each of the possible alternatives. The actors and directors data we get
from Forbes Magazine.
The movies earnings were obtained from Box Office Mojo, we went through each of the
movies and obtained their weekly sales, runtime, days in theater, number of theaters it was aired
in, domestic lifetime gross as well as production budget. Rotten Tomatoes was the chosen website
to obtain the movie scores, both from critics and from the general audience, this was done in order
to be able to see if there was any disparity in the coefficient and to see which best represents the
movie share implications. One other variable that was obtained from Rotten Tomatoes was the
Certified Fresh award that they give a movie, the dummy variable was issued in order to see if this
award is relevant towards the turnout of the market share for the movies that were considered to
be of higher quality by the website. However, most of our data comes from The Numbers web
page.
The seasonal dummy that was considered was decided based on the seasonal effects that
subdue the choice of the consumer when selecting a movie at different times of year. There are
movies that are released with that specific consideration in mind, for example, the choice of
releasing movies during the summer period are linked to the big blockbusters and in general there
is foreseeable increase in revenue for the specific time period. The runtime variable was chosen in
order to account for the possibility of having art oriented films, these movies tend to be longer and
have a select audience which can interfere with the data. This issue is addressed more in depth on
Ainslie et al (2005). We should note that we analyze each of the variable ahead.
b. Model:
Following the specification stated in Ainslie et al. (2005), for the basic model we use a
combination of a random effects logit model with a sliding window. This is done to incorporate the
effect of the movies entering and exiting the theater market, representing the time the movies are
present at the theaters.
Doing this movie ‘life cycle’ is relevant because for each week there are different
competitors present in the market. Each of the weeks were movies in our data set were currently
airing are considered to be markets that the consumer would choose between the movie options
available. That is, every week there would be a selection of movies that the consumer could choose
and, as weeks progress, movies either enter the market by being released in theaters or are removed
from them. This structuring allows for the comparison between markets and a more accurate
consideration of the differences between movies, as they are being compared between those that
are aired in the same period.
Now, the mean utility of watching a movie is estimated through the market shares. The
markets shares would be given by
𝑀𝑖𝑡 =𝑒𝑇𝑖𝑡𝐼𝑖𝑡
𝑒𝑇0𝑡𝐼𝑖𝑡 + Σ𝑗𝑒𝑇𝑖𝑡𝐼𝑖𝑡
Where 𝑇𝑖𝑡 is the tickets that were bought to see a specific movie during a certain week, and
I represents if the movie was present or not in the theaters.
To specify the outside good, the total possible market for a movie had to be deduced. So
for that, we take the maximum number of movie goers for a given week during all the time frame,
and use that as the total market. This value is a real observed value during a certain week, and it
serves as a proxy of the potential demand for movies in theaters.
c. Data Analysis
As stated before, the data was structured considering each week as a different market. Each
week there would be a group of consumers that would choose between the options available. For
the period of study (2010-2013) the number of weeks accounts to 205 weeks. Movies released
before 2010 but still screening during 2010 are not taken into account. The sample consists of 667
movies for which we have most of the data, the number of movies aired in theaters varies from
year to year, therefore we used the ones for which most of the variables were available. The lowest
grossing movies are not considered to be relevant in our analysis. There are some observations
missing from the Budget variable as some of movie budgets were not reported, but nevertheless,
the total data set includes then 6671 observations.
We should also note that to limit the amount of data, we considered a window of 10 weeks
for each movie. We chose the number of weeks arbitrary as it could clearly show the lifespan of
the movie, with higher sales during the first weeks and then progressively dying out.
Not all the movies were present during the whole 10 weeks and some were present much
more. To control for this, we have also incorporated in the model the number of days the movie
was present in the theaters.
To have less volatile data, all the variables that had extremely large observations were
transformed to their logarithmic form.
There are also several dummy variables in the analysis. The way we constructed them is
stated below.
d. Results
First, we have decided to compare a normal linear regression with one that considers
random effects. We find that the model that in the model that considers random effects the
coefficients over all become seem to become more significant. Also, the impact of the variables in
the random effects model tends to diminish, suggesting that there could be some sort small bias
not accounted for in the linear regression. Given that, we choose to continue with the model with
random effects for the rest of our analysis.
Table 1 Comparison of Models
Linear Random Effects No Actors
lntickets 0.364*** lntickets 0.384*** lntickets 0.380***
-8.32 -9.43 -9.35
lnbudget 0.0767 lnbudget 0.0686 lnbudget 0.0747
-1.75 -1.65 -1.79
distributor -0.0183** distributor -0.0149* distributor -0.0151*
(-2.99) (-2.44) (-2.47)
time 0.00829*** time 0.00901*** time 0.00998***
-4.04 -4.19 -4.71
days 0.00843*** days 0.00775*** days 0.00797***
-11.32 -9.42 -9.73
theaters 0.000342*** theaters 0.000323*** theaters 0.000343***
-5.73 -5.62 -6.02
mpaa -0.0765 mpaa -0.0848 mpaa -0.0695
(-1.63) (-1.88) (-1.56)
genr e-0.0192 genre -0.0190 genre -0.0175
(-1.87) (-1.93) (-1.79)
director 0.708*** director 0.633*** director 0.625***
-6.58 -4.74 -4.68
actor 0.199* actor 0.205* season -0.00289
-2.44 -2.55 (-0.09)
season -0.00991 season -0.000708 _cons -15.31***
(-0.35) (-0.02) (-17.30)
_cons -14.91*** _cons -15.06***
(-15.96) (-16.93)
---------------------------- ---------------------------- ----------------------------
N 3870 N 3870 N 3870
t statistics in parentheses
*p<0.05,**p<0.01,***p<0.001
Looking at the coefficients from the random effects model, we find that the variables have
the following sings:
The number of total tickets sold has a positive effect on a movie’s market share. This means
that the more tickets sold in total, will increase a given movie’s market share in each
market. This variable is highly significant and, together with the director’s variable, that
has the highest impact on the market share.
The budget for a movie does not appears to be significant in this model, nor in the linear
regression. This could suggest that consumers are not attracted to movies by the amount
that the producers have invested in them. Indeed, in our sample we see cases where movies
with high budget do not perform well in theaters. We suspect the quality of the story the
movie narrates could be responsible for this, but with our data we cannot test this
hypothesis. However, the coefficient is still positive meaning that the quality of the movie
tends to be better for consumers when the budget is higher, but that can signify a number
of different factors. For example, when we take out the actor variable from the model, we
observe that the budget variable has an important change and becomes significant at the
10% level, this could be suggesting that it is now considering the higher salaries of popular
actors that we are not controlling for any longer in the model.
The dummy for distributors shows that when the distributor is bigger, the market share is
affected in a positive way. We constructed this variable by assigning numbers from 1 to 22,
in order of size (i.e. movies produced by year) to the distributors. A negative coefficient
implies that the closer a distributor is to being to 1 (the biggest distributor), it will have
more market share.
The variable for time is highly significant as well. This is goes against the intuition that
longer movies (i.e. art movies) do better than shorter ones (i.e. blockbusters). However,
when we control for audience rating this changes and it becomes highly insignificant,
suggesting that the less popular movies, in this case art movies, usually have a lower
audience rating, so we consider that this effect is being controlled for when we introduce
the audience rating variable. For the following analysis we use the audience rating instead
of time as a variable.
The number of days a movie has in theaters highly significant and this agrees with the fact
that the most popular movies, the ones that perform better in theaters, have a longer
lifespan.
The maximum number of theaters that play at the same time a movie is an indicative of
how highly demanded a movie is, therefore we find a positive effect and a high level of
significance for this variable.
The MPAA ratings dummy does not provide any significant effect. It has a negative sign
which suggests that, given the way this variable is constructed (1 the lowest MPAA rating
G, and 5 the highest MPAA rating NC-17), consumers prefer higher rated movies. This
might be driven by the amount of audience there is for each rating. Lower rating suggest
family friendly movies which experience could be more expensive ticket wise, for parents
bring their families, in contrast to higher rated movies where children are not aloud, thus
making the experience less expensive.
For genre there does not seem to be a significant effect. Given the way the variable was
constructed (See appendix) we cannot determine the effect that is being captured.
The director and actor dummies are significant in the model which suggests that audiences
prefer movies by popular directors and with popular actors. We should note that the effect
that the directors have is larger in terms of market performance. This would mean that
audiences prefer movies with more popular directors than actors, this hypothesis is line
with what was said before about the story the movie narrates. Directors who are more
popular would tend to direct movies with more engaging stories, making their movies more
attractive for the consumers.
The season dummy does not seem to be significant at all, it is the least significant variable
of the model. Given the way it was constructed, a more positive coefficient value would
suggest that consumers go to movies later in the year. This does not appear to be the case
when all the weeks are considered together in the analysis. We can see an ongoing weekly
market for movies throughout all the seasons.
We should also state that some of the variables we thought of including in the analysis did not
have any value. We are referring to the alternative ratings Rotten Tomatoes provides, neither the
critiques rating nor the certified fresh award gave additional information to the analysis. Only the
audience rating was significant in our results. As can be seen in Table 2.
Table 2 Ratings
All Ratings Just Audience Rating
lntickets 0.297*** lntickets 0.300***
-7.21 -7.29
lnbudget 0.0984* lnbudget 0.0959*
-2.38 -2.32
time 0.00103 time 0.00127
-0.45 -0.56
distributor -0.0120* distributor -0.0120*
(-1.98) (-1.98)
days 0.00453*** days 0.00437***
-5.05 -4.95
theaters 0.000431*** theaters 0.000430***
-7.44 -7.42
mpaa -0.0504 mpaa -0.0548
(-1.11) (-1.23)
genre -0.0132 genre -0.0131
(-1.36) (-1.35)
director 0.601*** director 0.590***
-4.54 -4.47
actor 0.217** actor 0.215**
-2.73 -2.71
season 0.0126 season 0.0162
-0.4 -0.51
rt_c 0.000137 rt_a 0.0219***
-0.07 -9.7
rt_a 0.0229***
-7.59
rt_cf -0.0672
(-0.66)
_cons -14.91*** _cons -14.88***
(-16.93) (-16.91)
---------------------------- ----------------------------
N 3870 N 3870
---------------------------- ----------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
e. Further Analysis
Looking to continue the analysis with the data we have in hand, we have decided to apply
certain changes to the model we used so to see the different effects of them in our variables.
II.e.i. Using week number 1 and 5 to calculate market share
First, we consider using week number 1 and we find that all the variables are significant
except for the budget and genre variables. This implies a different behaviour of audiences during
the ‘hype’ period. One interesting factor that we can note is that the audience rating variable for
the first time diminishes in significance. Being before significant at the 1% level, it is now
significant at the 5% level. This is mainly because audiences have not yet seen the movies during
their first weeks. Now, this could imply that the ex-ante attractiveness of the movies are given by
different factors than the rest of the weeks. A clear example is what we see with the actor variable.
It is now highly significant unlike before and has a coefficient that drives a larger effect than the
director variable. This indicates that consumers are driven to movies by the presence of actors
more than directors.
The contrast is notable when we do the same analysis for week number 5. Consumers
during other weeks seem to have a lesser response to actors. The director variable has now a
significantly higher effect on the market share. Given that the “hype” period is over and that now
reviews of the movies exist (as can be noted from the audience rating being highly significant
again) movie goers prefer more popular directors than actors, although there is still a high effect
coming from both variables. This again is in concordance with our hypothesis that audiences prefer
stories.
Another difference we can see is in the seasons, this variable is now highly significant. For
the regression of week number 1, the coefficient is negative which indicates that audiences prefer
to go to movies in the first week later during the year, contrary to what happens to the regression
of week number 5 when audiences seem to prefer to go to movies on the 5th week of their release
earlier during the year. This would change the way one expects the sales by week to behave and it
would depend on the time of the year.
Table 3. Week 1 vs. Week 5
Week 1 Week 5
lntickets 0.446*** lntickets 0.392***
-23.6 -19.24
lnbudget -0.0135 lnbudget 0.147***
(-0.77) -7.73
rt_a 0.00237* rt_a 0.0280***
-2.5 -27.3
distributor -0.0239*** distributor -0.00193
(-8.83) (-0.65)
days -0.00515*** days 0.0115***
(-13.15) -26.79
theaters 0.000991*** theaters 0.000380***
-37.73 -13.43
mpaa -0.0869*** mpaa 0.0347
(-4.41) -1.62
genre 0.0127** genre -0.0107*
-2.96 (-2.31)
director 0.114 director 0.399***
-1.93 -6.26
actor 0.211*** actor 0.211***
-6.03 -5.6
season -0.105*** season 0.0484**
(-6.50) -2.83
_cons -12.21*** _cons -17.54***
(-30.81) (-40.75)
---------------------------- ----------------------------
N 3870 N 3831
---------------------------- ----------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
II.e.ii. Changes by year
Another additional analysis that was made was regarding the possibility of changes in the
coefficients between the chosen periods of study, which is to assess the possibility of varying
motivations given the different years. In order to accomplish this, a regression was run for each
year, 2010 through 2013. It is interesting to note that although there is an expected similarity
between the years, there are changes in almost all of the variables.
For the ticket variable, there is a decreasing pattern in the coefficients from years 2010-
2012, but a rise back, although not to the previous levels, on the year of 2013, while the variable
is statistically significant on all of the years. As for the budget variable, the coefficient sees a rise
during the same period, but a fall on 2013, but as seen by the z statistic, the significance of the
variable is quite volatile, only the years 2011 and 2012 show actual significance. For time, the
significant coefficients, years 2011 to 2013, showed a fairly similar positive correlation, with some
change during the year of 2011, although not a substantive one. This is rather expected as variations
in the runtime of the movie is a factor that should not theoretically bare too much relevance to the
choice throughout the years. As for number of days the movie was in theater, the statistical
significance of the variable is seen throughout all of the different years, in addition to that the
variation shows to be rather timid, with volatile increases and decreases but nothing that could be
interpreted as game changing for the industry, which is also something that was expected. The
MPAA variable showed to have no relevant statistical significance throughout the years, although
the correlation showed to be negative with little variance throughout as well.
A similar interpretation can be seen for the genre, as the statistical significance is mostly
negligible, as well as the variation between years. The only substantial statistical significance
found for the director was on the year of 2011, which was also the year in which the positive
coefficient was the greatest from the selected time period. As for the actor variable that denotes
the most paid actors/actresses according to Forbes, 2012 was the most statistically significant
coefficient while 2011 also showed to have some significance, both were positive with an increase
from 2011 to 2012. Finally, the season variable only seemed to be significant on the year of 2010,
with a negative statistically significant coefficient.
These results show that although there might be a perception that taste might be relatively
constant throughout the years, with an expected minimal change throughout, it can be shown that
this is not necessarily the case. There are, of course, several factors that could have led to these
annual differences, and although the scope of this paper does not allow for us to dwelve into the
particulars of each year. These results allow us to show that changes do occur throughout the years
and this begs for a greater adaptability of the different movie producers in order for them to account
for these variations.
Table 4. Comparisons by year
2010 2011 2012 2013
Coefficient z Coefficient z Coefficient z Coefficient z
lntickets 0.8687928 7.53 0.4019193 5.38 0.1807597 2.34 0.2753235 3.94
lnbudget 0.0860433 1.09 0.1440872 1.88 0.151552 1.75 -0.035394 -0.39
time -0.0018412 -0.41 0.0076913 1.98 0.0105089 2.18 0.0118719 3.43
days 0.0047401 2.82 0.0096235 6.13 0.0073471 3.17 0.0114331 7.73
theaters -0.0000929 -0.65 0.0002578 2.56 0.000468 3.73 0.0003147 2.68
mpaa -0.0868771 -1.13 -0.0276027 -0.39 -0.1367044 -1.19 -0.1088532 -1.17
genre -0.0192801 -0.9 0.009494 0.56 -0.0300594 -1.48 -0.0435087 -2.5
director 0.1717506 0.54 0.6139903 2.37 0.3921624 1.35 0.210772 1.05
actor 0.1111071 0.72 0.2563602 1.68 0.3556932 2.16 0.132454 0.96
season -0.1334445 -1.79 0.0173764 0.37 -0.069815 -1.14 -0.0144713 -0.27
_cons -20.30811 -10.97 -16.94082 -10.4 -13.72431 -7.71 -11.97092 -6.98
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
II.e.iii. Dropping distributor number 1 from the regression
After the change of removing the distributor, most of the coefficients seem to follow a
similar pattern. They show that the choice for the movies for distributor number 1 seemed to follow
the taste norm used for the other options, some important disparities can be highlighted. One
important change was the new role that the MPAA rating plays on deciding the movie choice, the
reasoning behind this can point to somewhat of a consumer loyalty towards that distributor,
meaning that the MPAA rating might not come into play as much when deciding for the movies
from that specific distributor, whereas it plays a more relevant role in the choice of the movies
from the remaining distributors. Another change that can also point towards the importance of the
distributor and, is the increase in the role that the director behind the movie plays in the choice the
consumer takes. A lower effect when the distributor is included shows a similar effect to that of
the MPAA rating, that is, who the movie's director is plays a lesser role in the consumer choice,
while it has a greater relevance on the choice of movies that do not include the specific distributor.
Table 5. No Distributor 1
No Distributor 1
lntickets 0.305***
-7.41
lnbudget 0.102**
-2.63
rt_a 0.0224***
-10.62
distributor -0.0117
(-1.95)
days 0.00419***
-4.8
theaters 0.000425***
-7.36
mpaa -0.0499
(-1.14)
genre -0.0122
(-1.27)
director 0.592***
-4.53
actor 0.223**
-2.85
season 0.0212
-0.65
_cons -14.95***
(-17.11)
----------------------------
N 3870
----------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
II.e.iv. Different Budgets
We now analyze the results for when the budgets are higher and lower than
US$ 50,000,000. Changing this variable has a very interesting effect on the output of the
regression. We focus on two variables here. The director and number of theaters variable. When
the budget is less than US$50 million, the directors appear to gain most of the responsibility of
having a movie that performs well in theaters. The director variable has the highest coefficient
here. This is important to note for when the budget is low, most of the movie’s success relies on
the director. Again, we repeat our hypothesis of the importance of the story of the movie. This,
however does not mean that the movies with low budgets have a widespread release among
theaters. The number of theaters variable s not significant here when the budget is low, contrary to
when the budget is +US$50 million. Movies with higher budgets have wider releases among
theaters.
Movies with higher budgets tend to last longer in theaters as well.
Table 6. Budget differences
Budget >US$ 50
Million
Budget <US$ 50
Million
lntickets 0.0393
lntickets
0.613***
-0.73 -9.72
lnbudget 0.101 lnbudget 0.0488
-0.72 -0.94
rt_a 0.0208***
rt_a
0.0181***
-5.64 -6.61
distributor -0.00380 distributor -0.0139
(-0.30) (-1.94)
days
0.00773*** days 0.00252*
-4.24 -2.31
theaters
0.000578***
theaters
0.0000963
-4.61 -1.24
mpaa 0.0453 mpaa -0.0219
-0.54 (-0.41)
genre 0.0161 genre -0.0191
-0.99 (-1.48)
director 0.350
director
0.686***
-1.45 -4.18
actor 0.185 actor -0.00903
-1.72 (-0.07)
season -0.0330 season 0.0135
(-0.68) -0.32
_cons -
11.67***
_cons -
17.53***
(-4.47) (-14.32)
----------------------------
---------------------------
-
N 1435 N 2347
----------------------------
---------------------------
-
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
III. Conclusion
The purpose of this paper is to study the movie theater market in a market share context
that we have done by grouping movies that were present in a same week. We have done that by
incorporating a 10 week
Analyzing this market this way has given us some interesting results. It has shown the
importance of certain variables before and after the movie is released a criticized. Actors appear
to have a more important role during the first week of the release, and directors show to have it
more in later weeks. We also found a possible behavior of consumers depending on the moment
of the year, going to movies on their first week later during the year, and going during later weeks
earlier in the year. We mention a hypotheses of the audience preference for story rather than other
variables as they appear to prefer directors over actors in several instances, and we suggest there
is a connection between more popular directors and more attractive stories.
We also encountered some limitations during our analysis. The other ratings provided by
Rotten Tomatoes could be compared to some data from other awards given in the movie industry,
to see if they do actually give some extra information about the movies. Another limitation is the
way we constructed some of the dummies to see if that characteristic was significant in the model,
especially with the genres which did not appear to be relevant at any moment.
IV. References
1. Andrew Ainslie Xavier Drèze Fred Zufryden. (2005) Modeling Movie Life Cycles and
Market Share. Marketing Science Vol. 24 (3), 508-17.
2. "Koreans Are No. 1 Moviegoers in the World." – The Korea Times. Web. 10 Dec. 2014.
<http://www.koreatimesus.com/koreans-are-no-1-moviegoers-in-the-world/>.
3. "Domestic Movie Theatrical Market Summary 1995 to 2014." The Numbers. Web. 10
Dec. 2014. <http://www.the-numbers.com/market/>.
4. "Moive Industry Key Statistics." ISIB World. ISIB. Web. 10 Dec. 2014.
<http://clients1.ibisworld.com.myaccess.library.utoronto.ca/reports/us/industry/keysta
tistics.aspx?entid=1245>.
5. "Moive Industry Industry At A Glance." ISIB World. ISIB. Web. 10 Dec. 2014.
<http://clients1.ibisworld.com.myaccess.library.utoronto.ca/reports/us/industry/atagla
nce.aspx?entid=1245>.
6. "Moive Industry Competitive Landscape." ISIB World. ISIB. Web. 10 Dec. 2014.
<http://clients1.ibisworld.com.myaccess.library.utoronto.ca/reports/us/industry/keysta
tistics.aspx?entid=1245#ID>.
V. Appendix
MPAA
1. G
2. PG
3. PG-13
4. R
5. NC-17
Genre
1. Action
2. Adventure
3. Black Comedy
4. Comedy
5. Concert/Performance
6. Documentary
7. Drama
8. Horror
9. Musical
10. Romantic Comedy
11. Thriller/Suspense
12. Western
Distributors:
1 Warner Bros.
2 Walt Disney
3 Universal
4 Sony Pictures
5 20th Century Fox
6 Lionsgate
7 Paramount Pictures
8 Weinstein Co.
9 Relativity
10 FilmDistrict
11 Open Road
12 Fox Searchlight
13 Focus Features
14 CBS Films
15 Sony Pictures Classics
16 Pantelion Films
17 Roadside Attractions
18 A24
19 IFC Films
20 UTV Communications
21 Eros Entertainment
22 Magnolia Pictures
23 RADiUS-TWC
24 Others