Star Effect on Revenue, Profit in Movie Industry

ACEI 2012 Kyoto, Japan
<strong>Star</strong> <strong>Effect</strong> on Revenue, Profit in Movie Industry
Preliminary
* Please do not cite, quote or circulate.
May, 2012
Seungkook Roh
82-10-2800-4319
skroh@kaist.edu
Graduate School of Culture Technology, KAIST, Deajeon, Korea
Wonjoon Kim
82-42-350-4336
Wonjoon.kim@kaist.edu
Department of Management Science, KAIST, Daejeon, Korea
* I would like to thank Namil Kim of KAIST for a lot of many helpful comments, Mehyun Kim
of KOFIG for offering useful data, George A. Furst of KAIST and specially Sunyoung Hong of
KODIT for editing. I am also grateful to graduate school of culture technology of KAIST. I
retain the responsibility for all errors and omissions in this paper.
1

Abstract
Until now, researchers have studied the methods to forecast financial success and bigger
revenues. Among many success factors, it is still unclear that star effect on power has a positive
impact on the financial performance. This study provides a better framework for predicting financial
performance of movies through star power. To solve the current controversies over star effect on
revenue and profit, this study focuses on revenue as well as profit and compares the star effect on each,
separately, by analyzing time-series and cross-sectional movie data. And we find 1) star effect is
positively associated with revenue, and 2) star effect increases production costs so that it decreases
profit of movies.
Keywords: <strong>Star</strong>, <strong>Star</strong> power, Motion Picture Industry, Movie Industry
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1. Introduction
Movies are the uncertain products to both consumers and film makers. Consumers do not
know whether it will be a good experience before watching and enjoying them(1, 2). Also, no matter
how bad it is, they cannot get movie tickets refunded after a film starts. Similarly, film makers take
considerable risk since the distribution of revenue in the movie industry is highly skewed. An
example is, the top 20% of movies earn approximately 80% of the total revenue in the motion picture
industry(1). Thus, one big hit can offset all the losses due to failures. Because of this unique feature of
the film industry, many investors and researchers have tried to find the critical factors necessary to
make a commercially successful film. Among the various determinants, an established audience,
presence of major stars, advertising, publicity or word-of-mouth and awards have received significant
attention in literature(2, 3). A number of other factors, such as movie genre, critics’ ratings, MPAA
ratings and others influence movie sales as well (1, 2, 4, 5). However, it is still unclear whether star
power has a positive impact on the financial performance. There have been many approaches for
finding the relationship between the success in film and star casting, each one yielding different
results. Some provide evidence that star power has a positive impact on revenue (6-10), while others
maintain that it has no impact on box-office performance (2, 11-13). Those who believe in the
positive impact of star power are willing to pay millions of dollars for casting a super star. In contrast,
those who doubt the effect invest money on other things. Therefore, Hennig-Thuran et al.(2007) also
reviewed that the impact of star power on movie success remains a contested issue as other
researchers question this relationship
Accordingly, the major purpose of this paper is to reexamine the star power in the movie
industry and extend previous research by examining the relationship between star casting and revenue
and profit rate. Also, we analyze how revenue and profit rate vary according to the magnitude of star
power.
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The rest of this paper is organized as follows: Section 2 provides conceptual background and
hypotheses based on a great deal of empirical literature on movies. Next, we develop a model to test
these hypotheses and analyze them with data from the Korean film market in section 3. Section 4
summarizes the result. Finally, section 5 summarizes overall findings gathered through this study and
includes suggestions for further research.
4

2. Literature Review on <strong>Star</strong> Power of Movie Industry and Model Constructs
In prior research, most authors use "revenue" as a dependent variable when they prove the
relationship between star power and performance. However, revenues are determined by audiences,
profits are determined by revenues minus costs(14). That is, high payment for a star guarantees high
expense. For example, Kevin Costner’s Water-world which costs $170 million and earned $88 million
U.S./$166 million worldwide had a low rate of return(14). As a result, even successful movies do not
always guarantee high net profit after paying for star guarantee. From a commercial point of view,
profit rate is a more appropriate variable than revenue for measuring financial performance. In the
previous research, S. Albert(1999) mentioned that because of a lack of information on budgets, he
cannot use profits as the measure of success. Therefore it is important to analyze the effect of star
power using both revenue and profit rate as dependent variables(15). This study presented a positive
relationship between revenue and star power, but it did not find a distinct relationship between profit
rate and star power. Yet these studies do not sufficiently explain how revenue and profit vary
according to the magnitude of star power.
2.1. Positive <strong>Effect</strong> of <strong>Star</strong> Casting Versus No <strong>Effect</strong> of <strong>Star</strong> Casting On Revenue
Many studies provide empirical evidence that star power is significantly related to revenue,
in that stars attract more audiences (3, 14, 16-20). Albert studied the role of the star and his model
suggests that stars are significant as they attract a number of fans(14). For this reason, some directors
delay for several years the making of a film until a specific actor or actress agrees to join their movie.
Also, the studies find that the more famous the actor is, the higher the revenue the movie makes. On
the other hand, other studies indicate that star casting has no effect on revenue (8, 18, 21, 22) and
Ravid(1999) examined a signaling model of the role of stars and concluded that stars play no role in
the financial success of a film(15). For example, movies such as the Jaws, <strong>Star</strong> Wars, Titanic, Shrek
were the biggest box office hits without a super star. Furthermore, Avatar recently released in
Hollywood, and several Korean movies such as Friend (2001), King and the Clown (2005), Old
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Partner (2008) support the finding that revenue is not associated with the involvement of famous
actors. Also, Elberse(2007) cannot find evidence of contributions of stars to movie success through
the online fantasy HSX market(9). This information is summarized in Table 1. In other words, some
movies without famous actors were not a success, and several movies featuring well-known stars were
also not commercially successful. However, although a film is a big failure at the box office,
contracted salaries of stars and product costs cannot be reduced or collected. Therefore, it is obvious
that a big budget for casting famous actors or actress may have a negative effect on economic
performance, given that star power is insignificant to draw audience. We thus propose the first set of
hypotheses:
Table 1. Literature source Related to Positive and Negative <strong>Effect</strong> of <strong>Star</strong> Power on Movie Industry
Researcher
Positive No effect
Rosen, 1981
Prag and Casavant, 1994
De Vany, 1999, 2004
Kelwick, 2002
Elberse, 2007
H1: A star power influences positively movie’s revenue.
2.2. <strong>Star</strong> Casting and Profit Rate
6
Ravid, 1999, 2004
Shugan, 2004
Ainslie, 2005
Brewer, 2009
No matter how big the revenue, no investors want to provide money to make films for which
net profits are zero. Certainly, they want to receive a high percentage of return on investment.
However, immoderate and careless budget cuts for casting could cause a falloff in the quality of the
film. The reputation and popularity of a star actor generally reflects their superb acting skills known
from in previous films(8). Many award winning films have high production costs, which seem to
indicate that high negative cost is attributed to the high quality of the film(2, 23). That is, negative
cost and marketing cost for film making ‘pays off’ for maximization of profits(8). In this context, the
effect of star casting should be analyzed for profit rate, not revenue, when the quality of the movie

and commercial success are considered. Since the direct measure of net profit after deducting every
cost from revenue is difficult to obtain, various studies evaluate economic performance, focusing on
revenue rather than net profit. However, if the payment for casting stars is more costly than the money
amount that the stars draw and receive from sales of movie tickets, it would be doubtful to say that the
movie is successful. For this reason, Ravid(1999) used profit rate instead of revenue to assess the
effect of star casting in relation to economic performance of the movie. Ravid reports that revenue is
not consistent with profit rate due to the different amount of payment for casting. As shown in the
scatter diagram below Figure 1, empirical data collected in this study also show that higher revenue
does not always guarantee high profit rate.
PR
0 .2 .4 .6 .8 1
0 5 10 15
LNREV
Figure 1. Relationship between Revenue and Profit rate
Data in this study Table 2 also shows that revenue is insignificantly related with profit rate.
Table 2. Correlation between Revenue and Profit rate
Profit Rate Revenue
Profit Rate 1.0000 -
Revenue 0.377 1.0000
*Correlation is significant at the 0.01 level (2-tailed)
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Ravid suggested that high expense for making films generates high revenue, but it does not
always increase profit rate(1, 15). Therefore, it is desirable to analyze revenue and profit rate in
relation with the influence of star power.
Based on these discussions, we have the following hypotheses:
H2: A star power influences negatively movie’s profit.
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3. Methodology
3.1. Data
In this study, data was collected from the Korean Film Council (KOFIC) and NAVER Movie,
a popular Korean search portal. The sample consists of 214 films out of the 349 Korean films released
during 2008-2010. We removed 135 movies with life-cycle less than four weeks in theatres since on
average the revenue of the movies during the first four weeks account for more than 90% of their total
revenue(24). In addition, each of 214 films has their own daily WOM, the opening number of screen,
revenue and profit rate for 28 days. Thus, the total amount of data becomes 5,992.
Specifically, the primary source of data for star, movie rating[G, under 12(similar to PG13),
15(similar to R), or 18(similar to R)] by the Korea Media Rating Board(KMRB) like Motion Picture
Association of America(MPAA) rating in United States of America, release date of movies (weekday
or weekend), distributor (major or minor), producer (major or minor), and volume of Word-Of-Mouth
(WOM) about volume and critical review is the NAVER Movie site (http://movie.naver.com)(5).
NAVER, as the most popular portal in Korea, surpasses any other portal sites such as Google and
Yahoo in market share, thus it helps decrease bias in the demographic composition of the Web site’s
visitors. Also, revenue, profit rate and WOM volume are archived and indexed numerically by the
dates, so it is easy to collect data. The negative expense and marketing expense data are both collected
from articles, movie public relations (PR) information and papers offered from KOFIC. The data
regarding the number of screens in the theater is collected from annual report and official web-site of
KOFIC.
3.2. Variables
3.2.1. Dependent Variables
Dependent variables in this study consist of two variables which are revenue and profit rate.
Each variable has same independent variables to compare with one another concerning a star effect.
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3.2.2. Revenue
Revenue is a good indicator to assess economic performance since it contains not only box-
office gross but also income through other distribution channels, so-called “window effect” (e.g.
release in foreign market, DVD, pay television etc.)(1). Thus, revenue has been used as a dependent
variable in the earlier studies. However, the window effect in Korean film industry is insignificant. 88%
of revenue earned from box-office receipt in 2009, and the percentage of revenue from sales of theatre
tickets has been increasing since the year 2000. In comparison with other countries, widely circulated
illegal download of movies and pirated copies impede the growth of home video market (VHS, DVD),
remaining in 0.86% of total revenue in movie industry as of 2009. Also, exports to foreign markets
tend to be increasing, but it reached about 4.12% in 2009 (KOFIC, 2009). For this reason, revenue in
this study is confined to the box office receipt, the total amount of money from the sale of tickets for
movie.
3.2.3. Profit rate
Ravid(1999) defined that profit rate is the return on investment measure, total revenues
divided by the negative cost(15). Here, negative cost is a production cost for final film, including
actors’ salaries, filming costs editing costs etc. Negative cost shows a highly positive correlation with
star casting since negative cost includes star casting(22). Also, negative cost is positively related to
revenue(2).
3.2.4. Independent Variables
In this study, independent variables consisting of several variables are used in many previous
research to analyze the correlation with the commercial performance of movie.
3.2.5. <strong>Star</strong> Power
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In the previous studies, the star power variable was used to explain box office success.
Moreover, “<strong>Star</strong>s are a long-standing feature of the movie business and the top ones command large
salaries”(25). However, it has always been difficult to define star power. So, many different
approaches to measure star power have been adopted from reviewing the actor’s box office history,
their movie award records and so on(25, 26). Among many methods, we adopted Elberse’s method to
measure star power. This is based on the numbers of audience for the most recent years. That assesses
star power quantitatively in each movie based on the performance in previous films.
3.2.6. Other variables
Except for star power, we adopted genre, movie rating, weekend, distributor, producer, the
opening number of Screen, word of mouth (WOM), critical review as control variables in this study (2,
3, 5, 8, 9, 18, 27-32). Moreover, among the variables mentioned above, we converted revenue, the
opening number of screens, WOM, star power into logged form as these variables are logged due to
their ‘long right-tailed nature’(2, 33).
Table 3. Descriptive Statistics of this research
Variable Mean Std. Dev. Min Max
Dependent Variables
LNREV 7.069745 3.400228 0 13.39704
PR 0.01304 0.031892 0 0.907299
Independent Variables
LNSCR 3.494642 2.140678 0 6.714171
LNWOM 2.218691 2.078476 0 7.489971
day 14.5 8.078421 1 28
weekend 0.428571 0.494913 0 1
Distributor 0.453271 0.497853 0 1
producer 0.070094 0.255326 0 1
R12 0.242991 0.428925 0 1
R15 0.378505 0.485055 0 1
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3.3. Model
R18 0.252336 0.43439 0 1
GA 0.046729 0.211075 0 1
GT 0.140187 0.34721 0 1
GM 0.126168 0.332067 0 1
GD 0.35514 0.478596 0 1
GH 0.046729 0.211075 0 1
LNSTAR 9.834363 5.959906 0 19.79363
CR 7.567383 1.39874 2.6 10
Even though a lot of authors have studied the impact of star power as one of several factors
on movie success discussed in the previous research, these were mainly done through multiple
regression analysis using cross-sectional data (8, 15, 18, 20, 21). However, movie data has to be
analyzed by time-series and cross-section together because new movies influences their revenue and
profit rate are statistically dependent each other at the given time. Thus, We estimate that the
relationship between revenue and star using Panel regression which is usually carried out on Time-
Series Cross-Sectional data that has observations over time for several different units or ‘cross-
sections’(34). In addition, this data has heteroskedasticity in error term. Thus, we used a Generalized
Least Squares (GLS) method to calculate effective estimates. We developed two empirical models to
test star power influence on revenue and profit rate as follows:
3.3.1. Relationship between Revenue and <strong>Star</strong>
In the first model, we considered an estimated panel model for daily box office revenue,
including Table 4 to define variables, is as follows:
(�) ����� �� = ����� + � ������ �� + � ��� �� + � ���� �� �� + � ������� � + � �� �
+ � �� � + � �� � + � �� � + � �� � + � ��
12

Table 4. Variables Utilized in Above Equation
Variable Description of measure
LNREV Daily Revenue
LNSCR Daily opening number of each movie’s screens for 28 days
CR Critical review
LNWOM Daily WOM volume for 28 days
LNSTAR Actor’s box office history for last 3years
R Movie rating (Rate 12, Rate 15, Rate 18)
W Weekend (Dummy variable, weekday 0, weekend 1)
G Genre dummy (Comedy 0, Action, Thriller 1, Melodrama 2, Drama 3, Horror 4,
Others 5(SF, Animation, Documentary etc.)
D Distributor dummy (Major distributor 1, Minor distributor 0)
P Producer dummy (Major producer 1, Minor producer 0)
Where t standing for each day is (1,2,…28) and i standing for each movie is (1,2,… 214). The model
is composed of daily opening number of each movie’s screens for 28 days, critical review, daily
WOM volume for 28 days, star effect, movie rating dummy, weekend, and genre dummy, distributor
dummy, and producer dummy.
3.3.2. Relationship between Profit Rate and <strong>Star</strong>
In the second model, we estimate that the relationship between profit rate and star using
panel analysis. The estimated panel model for daily box office revenue, including Table 5 to define
variables, is as follows:
(�) �� �� = ����� + � ������ �� + � ��� �� + � ���� �� �� + � ������� � + � �� � + � �� �
+ � �� � + � �� � + � �� � + � ��
Table 5. Variables Utilized in Above Equation
Variable Description of measure
PR Profit Rate
LNSCR Daily opening number of each movie’s screens for 28 days
CR Critical review
LNWOM Daily WOM volume
LNSTAR Actor’s box office history for last 3years
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R Prohibition code dummy (Rate 12, Rate 15, Rate 18)
W Weekend (Dummy variable, weekday 0, weekend 1)
G Genre dummy (Comedy 0, Action, Thriller 1, Melodrama 2, Drama 3, Horror 4,
Others 5(SF, Animation, Documentary etc.)
D Distributor dummy (Major distributor 1, Minor distributor 0)
P Producer dummy (Major producer 1, Minor producer 0)
Where t standing for each day is (1,2,…28) and i standing for each movie is (1,2,… 214). The model
is composed of daily opening number of each movie’s screens for 28 days, critical review, daily
WOM volume for 28 days, star effect, movie rating dummy, weekend, and genre dummy, distributor
dummy, and producer dummy.
14

4. Results and Discussions
4.1. Revenue and <strong>Star</strong>
Table 6 summarizes the estimated empirical relationship between explanatory variables and
box office revenue. <strong>Star</strong> power, the number of opening screens, WOM, Distributor and critical ratings
are all positively related to revenue. Also, the empirical result indicates that Drama Genre generate
higher revenue than movies rated as R12, R15. However, producer, R18 and Genre (action, Thriller,
melodrama, horror) are not significantly associated with revenue. As for genre, comedy films draw
more audience than other genres.
Table 6: Determinants of Revenue
Dependent Variable: Log(Revenue), N=5,992
Panel Analysis (Random-effects Generalized Least Squares regression)
Variable Coef. Std. Err. z P>Z
LN<strong>Star</strong> 0.007521* 0.002839 2.65 0.008
LNSCR 1.233673* 0.01119 110.25 0
LNWOM 0.324035* 0.010905 29.71 0
weekend 0.460477* 0.023889 19.28 0
Distributor 0.097941* 0.030826 3.18 0.001
producer 0.021941 0.048853 0.45 0.653
R12 -0.19515* 0.042877 -4.55 0
R15 -0.21239* 0.043779 -4.85 0
R18 -0.07825 0.049049 -1.6 0.111
GA -0.07131 0.062192 -1.15 0.252
GT -0.04353 0.044947 -0.97 0.333
GM -0.1205* 0.043889 -2.75 0.006
GD -0.20517* 0.031826 -6.45 0
GH -0.26863* 0.061593 -4.36 0
CR 0.127408* 0.009584 13.29 0
CONST 1.015788 0.092077 11.03 0
Notes: indicate the significance at 1% level *, 5% level **, panels: homoscedastic, no autocorrelation
As shown in table 6, this empirical study presents that star power is positively associated
with revenue (H1). This finding is equivalent to previous study regarding Hollywood movies(28).
Regardless of quality and production cost of the movie, moviegoers pay the same price for buying
15

tickets. Thus, moviegoers are more likely to choose the movies with a variety of attractions and
popularity such as well-known stars, directors, and state-of-the-art 3D technology in order to decrease
the likelihood of failure(2). For this reason, theatres show several marketable movies. This
monopoly of the screen limited the selection of audience and in turn movies with well-known stars
become box-office success. Thus, the movie industry has about 0.8 of Gini’s coefficient and is
therefore regarded as highly dangerous and uncertain.
4.2. Profit Rate and <strong>Star</strong>
Table 7 describes independent variables which influence profit rate. Variables such as
number of screens, WOM, weekend, and critical ratings are all positively related with profit rate.
However, star power, distributor, producer are negatively correlated with profit rate. Also, films rated
under G-rated movies generate higher profit than films rated R12. It is assumed that the genre of films
rated G are generally family drama, documentary, comedy, which produce movies with relatively
lower budget and therefor result in a higher profit rate. However, in the previous research regarding
movie ratings in Hollywood, G-rated and over R18 (R-rated in U.S.) movies have nearly equal
average rates of return. The reason of the difference of two movie markets resulted from a cultural
distinction of making R-rated movies each country. Usually R-rated movies are low-budget movies in
Hollywood while R-rated movies are middle or high-budget movies for adults in Korea. However,
R18 and horror movie are not significantly associated with revenue. As for genre, comedy films draw
more profitable than other genres.
Table 7. Profit Rate and Independent Variables
Dependent Variable: Log(Revenue), N=5,992
Panel Analysis (Random-effects Generalized Least Squares regression)
Variable Coef. Std. Err. z P>z
LN<strong>Star</strong> -0.00083* 8.81E-05 -9.43 0
LNSCR 0.002658* 0.000347 7.65 0
LNWOM 0.005268* 0.000339 15.56 0
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Weekend 0.008253* 0.000742 11.13 0
Distributor -0.00586* 0.000957 -6.12 0
Producer -0.00302** 0.001517 -1.99 0.046
R12 -0.00907* 0.001331 -6.81 0
R15 -0.00454* 0.001359 -3.34 0.001
R18 -0.00257 0.001523 -1.69 0.091
GA -0.00382** 0.001931 -1.98 0.048
GT -0.00589* 0.001395 -4.22 0
GM -0.00332** 0.001362 -2.44 0.015
GD -0.00772* 0.000988 -7.81 0
GH -0.00188 0.001912 -0.98 0.326
CR 0.002503* 0.000298 8.41 0
CONST -0.01055 0.002858 -3.69 0
Notes: indicate the significance at 1% level *, 5% level **, Panels: homoscedastic, no
autocorrelation
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5. Conclusion and future research
In the movie industry, profit rate as well as revenue is important for estimating ultimate
commercial success. Thus, this study compares star power with revenue and profit rate. We propose
two hypotheses: (1) stars influence positively revenue and (2) stars influence negatively profit rate.
This study examined the determinants of revenue and profit rate of Korean films for the years 2008-
2010. The revenue during these years shows a positive relationship with star power. On the other hand,
the profit rate shows an opposite result, which is high in a low level of star power and low in the high
level of star power. These results support that producers and investors need to consider star power to
make profitable movies in Hollywood. It would be not a good strategy to spend budgets for actors
with star power related to profit rate. In future research, we will extend our study to Hollywood
films and films of other countries to obtain a more universal conclusion.
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