Piracy and Movie Revenues: Evidence from Megaupload A Tale of the Long Tail?
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Piracy and Movie Revenues: Evidence from Megaupload
A Tale of the Long Tail?∗
Christian Peukert1 , Jörg Claussen2 , and
Tobias Kretschmer1,3
1
LMU Munich, Institute for Strategy, Technology and Organization
2
Copenhagen Business School, Department of Innovation and Organizational Economics
3
ifo Institute for Economic Research at the University of Munich
First Version: Oct 22, 2012
This Version: August 20, 2013
Preliminary
Abstract
In this paper we make use of a quasi-experiment in the market for illegal downloading
to study movie box office revenues. Exogenous variation comes from the unexpected
shutdown of the popular file hosting platform Megaupload.com on January 19, 2012.
The estimation strategy is to compare box office revenues before and after the shut-
down, controlling for various factors that potentially explain intertemporal differences.
We find that box office revenues of a majority of movies did not increase. While
for a mid-range of movies the effect of the shutdown is even negative, only large
blockbusters could benefit from the absence of Megaupload. We argue that this is
due to social network effects, where online piracy acts as a mechanism to spread
information about a good from consumers with low willingness to pay to consumers
with high willingness to pay. This information-spreading effect of illegal downloads
seems to be especially important for movies with smaller audiences.
Keywords: Piracy, Movie Revenues, Megaupload, Natural Experiment
JEL No.: L82, M37, D83
∗
Support for this research by NBER’s Economics of Digitization and Copyright Initiative is gratefully
acknowledged. We also thank audiences of seminars and conferences at LMU Munich, Paris School of
Economics, MaCCI Annual Conference 2013, IPTS Seville, UT Arlington, and Oliver Falck and Alex
Shcherbakov for useful comments and discussions. We thank Sandra Huber for research assistance. All
errors are ours. Peukert (corresponding author): c.peukert@lmu.de, Claussen: jcl.ino@cbs.dk, Kretschmer:
t.kretschmer@lmu.de.1 Introduction
In this paper we make use of a natural experiment in the market for illegal downloading
to study movie box office revenues. Exogenous variation comes from the unexpected
shutdown of the popular file hosting platform Megaupload.com on January 19, 2012.
Megaupload has been one of the most popular file hosting services worldwide account-
ing for 4% of the entire internet traffic (self-reportedly). Files uploaded to the platform
could be accessed via links, either as direct downloads or streams. While free download-
ing was limited in size and bandwidth, users could buy unlimited premium memberships.
Most of the users did not enter the website directly but were linked to it via other por-
tals. Just like Peer-to-Peer (P2P) networks, such as Napster or BitTorrent, Megaupload
has caused a controversial discussion concerning copyright infringement of the content its
users shared. Nevertheless, the arrest of the management team and seizure of the internet
domains in January 2012 came unexpected.
The effects of illegal downloading of digital content (piracy) are vividly discussed in the
digitization literature (Waldfogel, 2012; Greenstein et al., 2010). Theorists have looked
at the phenomenon from several perspectives (Peitz and Waelbroeck, 2006a). Some work
finds that firm revenues decrease due to copying, which in turn leads to lower incentives
to invest in quality in the long run (Bae and Choi, 2006). Other authors suggest that
piracy may actually benefit firms. Takeyama (1994) shows that unpaid copying may help
firms reach critical mass in network markets more quickly. Others have looked at how
illegal copying may help consumers make informed purchase decisions by allowing to find
a better match to their tastes. This is the ‘sampling’ effect (Peitz and Waelbroeck, 2006b).
Relatedly, Zhang (2002), Gopal et al. (2006) and Alcala and Gonzalez-Maestre (2010) offer
a more nuanced perspective. Unpaid copying lowers information costs of consumers which
then increases the market share of niche products.
According to a recent survey by Smith and Telang (2012), the results of the empirical
literature are also mixed. However, most papers find that piracy negatively impacts sales
of media products. For example, Danaher and Waldfogel (2012) look at the theatrical
release lag of the top ten movies in several countries relative to the US and find that longer
release lags lead to lower revenues. The effect is stronger in years in which BitTorrent was
1available. In a recent working paper Danaher and Smith (2013) look at average weekly
units of digital movie sales and rentals of two movie studies to study the impact of the
Megaupload shutdown. They find that both digital channels experience an increase in
units purchased after the shutdown.
Research has shown the importance of the long tail phenomenon in entertainment mar-
kets (Zentner et al., 2012), and the piracy literature has also looked at heterogeneity in
popularity. Oberholzer-Gee and Strumpf (2007) find that there is no significant difference
between the effect of piracy on music sales of popular and less popular artists. Bhattachar-
jee et al. (2007) find that the average time a music album stays on the sales charts decreases
after file-sharing technologies become available. However, their results also indicate that
albums promoted by ‘minor’ labels experience a significant positive shift.
In this paper, we want to combine these two perspectives when we look at the effect
of the Megaupload shutdown on movie box office revenues. Rather than looking at the
average effect across all movies, we explore heterogeneity in the effect. Our data comes
from boxofficemojo.com, a commercial provider of industry statistics. We observe weekly
revenues of a large set of movies in a variety of countries in many parts of the world from
2007 to early 2013.
We find that box office revenues of a majority of movies did not increase. While for a
mid-range of movies the effect of the shutdown is even negative, only large blockbusters
could benefit from the absence of Megaupload. We provide a number of robustness checks
to rule out alternative explanations using different specifications and additional data.
A mechanism that can explain these counterintuitive findings is that piracy has positive
externalities, where information about the quality of an experience good spills over from
pirates to purchasers. Once it becomes significantly less easy to consume pirated content
online, we would expect that at least some consumers convert to legal digital purchases or
start going to the movies. At the same time, the positive externalities vanish, making a
number of consumers (with non-zero willingness to pay) less informed about specific titles.
The net effect depends on how important the information-spreading externality is for the
performance of a specific movie. For blockbusters with huge advertising budgets the sales
replacement effect of piracy is probably much more pronounced than the word-of-mouth
2effect. For movies with smaller audiences it is likely to run the other way round.
We aim to contribute an alternative perspective to the emerging empirical literature
on the effects of piracy. We believe that the setting we study offers a unique opportunity
for causal identification. Our results have implications for theory and firm strategy in
practice, but may also contribute to the recent global debate on copyright in the digital
society.
2 Megaupload
The increasing availability of broadband Internet connections made online transfer of large
files feasible, leading to an upsurge in video downloading and streaming over the Internet.
This opened a new distribution channel for the movie industry, but at the same time also
enabled users to consume pirated movie contents.
P2P protocols such as BitTorrent originally had a leading role in the distribution of il-
legal content. The decentralized hosting of content on private computers makes shutdown
of those protocols hard and no single operator has to incur costs for infrastructure and
bandwidth. However, usage of P2P protocols requires installation of applications, recon-
figuration of network settings, and usually does not allow immediate streaming, making
P2P movie piracy difficult for inexperienced computer users. The emergence of filehosters
(also called cyberlockers) made consumption of illegal movie contents considerably easier
even for inexperienced users: no installation of applications and network reconfiguration
is necessary and many filehosters even allow direct video streaming. Using these services
is therefore not more difficult than watching a video on Youtube.
Megaupload has been the by far dominant filehoster alleged for distributing pirated
movie content. Founded by Kim Dotcom (formerly Schmitz) in 2005, it allowed users to
easily upload large files. This content could be made publicly available by distributing a
link to the uploaded file and the file could then be downloaded and or directly streamed
through the sister website Megavideo. Megaupload was financed through advertising rev-
enues as well as through premium subscriptions. In the free version of Megaupload, down-
load speed was limited and video streaming was interrupted for 30 minutes after 72 minutes
of streaming, refraining free customers from watching a full-length movie in one go.
3Figure 1: Megaupload Search Volume
90 100
90 100
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2010 2011 2012 2013
Relative Weekly Worldwide Search Volume Relative Weekly Worldwide Search Volume
Megaupload, Megavideo Megaupload, Megavideo
Source: Google Trends. Source: Google Trends.
Megaupload became widely popular and had (according to own statements) more than
50 million daily visitors, more than 180 million registered users, and captured 4% of total
Internet traffic. The fast growing popularity of Megaupload and Megavideo (which was
launched in August 2007) can also be observed with Google Trends data as depicted in
Figure 1.
Even though Megaupload claimed to run a legal business aimed at users distributing
legal content and offering to remove copyright infringing content on request, it was still
alleged to mainly distribute illegal contents. Chris Dodd, the chairman of the Motion
Picture Association of America (MPAA) claims: “By all estimates, Megaupload.com is
the largest and most active criminally operated website targeting creative content in the
world. [...] The site generated more than $175 million in criminal proceeds and cost
U.S. copyright owners more than half a billion dollars.”1 Even though direct visits to the
Megaupload website did usually not bring up pirated content, movies could be located
through search engines2 and to an even larger extent through link portals. These link
portals enable easy searching and browsing through links directed to filehosters. The
symbiotic relationship between Megaupload and the link portals created a grey area as
the link portals claimed to be legal as they don’t host any content and Megaupload claimed
1
MPAA press release, available at: http://www.mpaa.org/resources/e2fc0145-f17b-4df7-98b8-
ed136f65ea51.pdf
2
In January 2011, Google disabled the autocomplete function for ‘piracy related terms’ such as BitTorrent
or Megaupload. This explains the kink in figure 1. See http://torrentfreak.com/google-starts-censoring-
bittorrent-rapidshare-and-more-110126/
4Figure 2: Global Box Office Revenues Figure 3: Megaupload Popularity
25
2
20
1.5
15
1
10
.5
5
0
2007 2008 2009 2010 2011
0
Africa Asia Europe
2006 2007 2008 2009 2010 2011 2012
Latin America Oceania United States
Box office revenues in billion US$ Average Yearly MP per Broadband Subscriber
US and Canada, International Source: Google Trends, Google AdWords.
Source: MPAA Theatrical Market Statistics, 2010–2012.
to be legal as they take down illegal content when asked to do so.
Looking at the development of box office revenues in the US and Canada as well as
in international markets (Figure 2), surprisingly there is no obvious downturn: revenues
have been stable in the American markets and have increased significantly in international
markets. It seems therefore not straightforward whether the wide usage of Megaupload
did indeed lead to significant losses for the movie industry. Causal interference of the
effects of movie piracy on these long-term revenue developments is however difficult as
it is not possible to compare actual revenues with a hypothetical setting without movie
piracy.
3 The Shutdown of Megaupload
Even though causal inference of the effects of piracy is hard to achieve, we believe that
the shutdown of Megaupload is a well-suited exogenous shock which allows identifying the
actual effect of movie piracy on box office revenues. The Megaupload website was closed
down on January 19th 2012 after an indictment by a federal grand jury. On the same day,
raids were conducted in 8 countries, with search warrants being issued for 20 properties.
The founder of Megaupload and some of his managers were arrested in New Zealand and
company assets were seized. The shutdown of Megaupload did not only take the most
successful filehoster immediately offline, but it also created a major shock in the overall
5market for filehosters. Even though Megaupload was not incorporated in the US, the lease
of servers within the US was enough to allow Megaupload being persecuted by US law.
Many competitors of Megaupload feared legal action and immediately reacted by shutting
down or limiting their functionality. An example of such a limitation in functionality was
the filehoster Fileserve, which only allowed file downloads by the person who uploaded
the file, rendering the platform useless for the distribution of pirated content.3 Finally,
the shutdown of Megaupload was accompanied by massive press coverage, creating huge
public interest.4 This massive press coverage likely created a shift in consumer awareness
of what is illegal.
So the net effect created by the shutdown comes then i) from the largest filehoster
being taken down, ii) from many competitors stepping down voluntarily, fearing legal
action, and iii) from a likely shift of consumer awareness of what is illegal.
If we want to use the shutdown to identify the causal effects of movie piracy, we have to
be sure that the event was indeed exogenous to the involved parties. As no reports about
an expected shutdown leaked beforehand, we can be quite sure that the shutdown was
exogenous event to demand. Regarding Megaupload itself, we could not find any reports
on changes being implemented before the shutdown. Furthermore, the management team
did not try to escape to a safer country before their arrest, what they would probably have
done if they had been aware of the upcoming shutdown. Finally, although the MPAA was
seemingly involved in the investigations and the shutdown of Megaupload, it is hard to
believe that the movie industry could have affected the exact timing of the shutdown.
With more people being let in on the upcoming shutdown, also the risk of leakage would
have increased, dramatically reducing the chances of success. On top of that, the long
production cycles of movies makes strategic short-time reaction very difficult.
To sum up, we believe that the Megaupload shutdown was an exogenous shock for the
demand side, for Megaupload itself, as well as for the movie industry. We also made the
point that the shock is big enough to allow identification of the causal effect of piracy on
box office revenues.
3
See http://torrentfreak.com/cyberlocker-ecosystem-shocked-as-big-players-take-drastic-action-120123/
4
The large public attention can be observed by the peak in Figure 1 observed at the shutdown date.
64 Methods and Data
4.1 Empirical Specification
The estimation strategy aims at identifying the average treatment effect (AT E),
AT E = E[Rijt (St = 1) − Rijt (St = 0)] (1)
where Rijt denotes box office revenues of movie i in country j at time t, and St indicates the
shutdown of Megaupload. Simply comparing averages before and after the shutdown would
be sufficient if we could assume that revenues of movies before and after the shutdown are
independent, i.e. movie-, country- or time-specific factors do not change before and after
the shutdown. As an example, an obvious reason to doubt this that all movies experienced
the shutdown simultaneously, but at different stages of their lifecycle. Maximum weekly
revenues are typically reached in the very first weeks and demand then decays rapidly. Put
differently, box office revenues of a particular movie experience a different growth trend
almost by definition before and after the shutdown.
We can take care of this by conditioning on suitable covariates Xijt to arrive at an
unbiased estimate of the ATE, i.e.
AT E(xijt ) = E[Rijt (St = 1) − Rijt (St = 0)|Xijt = xijt ]. (2)
To estimate this effect in a regression framework, we assume a linear relationship, such
that
0
E[Rijt |St , Xijt ] = β0 + St β1 + Xijt β2 + (St Xijt )0 β3 , (3)
which implies
0
E[Rijt |St = 0, Xijt ] = β0 + Xijt β2 , (4)
0
E[Rijt |St = 1, Xijt ] = β0 + β1 + Xijt (β2 + β3 ), (5)
7and can be estimated via OLS to arrive at an estimate of the ATE given by
AT\
E(xijt ) = E[R|S =\
1, Xijt = xijt ] − E[R|S =\
0, Xijt = xijt ]
= β̂1 + x0ijt β̂3 . (6)
The set of covariates includes fixed effects for countries, years, calendar weeks, and movies
to remove time-invariant within-group variation. We account for the specific stage of the
lifecycle controlling for movie age. Of course it would be great to observe the number of
downloads/streams on Megaupload on a movie-level. In absence of this type of data, we
control for the average popularity of Megaupload in a given year and country. We further
explore the possibility that any effect of the shutdown is heterogenous across groups of
observations. To test the presence of a size effect, we include a country-specific measure
of movies being rather targeted at small audiences or huge blockbusters. The data are
described in detailed below.
4.2 Data
We construct our dataset from a variety of publicly available sources. Weekly data from
10,272 movies in 50 countries (see table A.1) spanning from 2007w31 to 2013w5 comes
from Boxofficemojo.com, a commercial provider of industry statistics. Our sample be-
gins with the launch of Megaupload’s video streaming service (Megavideo), which made
it considerably more convenient to watch pirated movies online. We match the revenue
data to IMDB, the leading internet platform for movie meta information, to obtain infor-
mation about the genre(s) international titles. Data from Google Trends and the Google
Adwords Keyword Tool is used to construct a measure of country-specific Megaupload pop-
ularity. Broadband subscription numbers come from the World Telecommunication/ICT
Indicators Database provided by the International Telecommunication Union (ITU). To
construct a robustness check that tests the proposition of a general trend in the availability
of pirated content online, we obtain movie-level information about the timing of illegal
supply from Thepiratebay.se (TPB), a leading link portal for BitTorrent.
84.2.1 Box office revenues
The variable of main interest is weekend box office revenues, measured in US dollars.
Weekends are not necessarily comparable across years, because the days of a weekend
do not always coincide with calendar weeks. We therefore construct a measure on the
calendar week level by dividing by the number of days of a weekend and summing this
number within calendar weeks. This of course relies on the assumption that all three days
of a weekend contribute equally to the total weekend revenues. Because the variable is
largely skewed (mean: $235,691, median: $11,821), we use the log in the regression.
4.2.2 Independent Variables
Shutdown The shutdown of Megaupload happened on Thursday, January 19th, 2012,
i.e. in the third calendar week. Revenue data for the third calendar week in 2012 refer
to January 20th to 22nd. We therefore define the post shutdown period as after 2012w2
and construct a corresponding dummy variable. 80% of our observations are from the
pre-shutdown period.
% First-Week Screens We measure movie size using information about exhibition
intensity of a movie in a given calendar week and country. We do not directly use absolute
numbers or market shares per country and week because such measures are endogenous
when theater owners can for example quickly adjust the number of screens as a response
to changes in demand. Using the exhibition intensity in the first week as a measure of
expected overall demand can mitigate this issue. For most countries Boxofficemojo reports
the total number of screens per movie and weekend, while for some countries we observe
the number of theaters.5 This is not the same, since one theater location may play a movie
on several screens. To ensure that we are not picking up this artifact in the estimations,
we relate the first-week screens (theaters) to the maximum number of screens (theaters) in
a given country. The resulting measure is a percentage where 1 indicates that the movie
has the biggest starting week of all (observed) times in a given country. The distribution
of this variable is skewed, with median of .08 and a mean of .14. It seems likely that the
5
These countries are Australia, Czech Republic, France, Germany, Italy, Spain and the United Kingdom.
9relationship between exhibition intensity and revenues has diminishing marginal returns,
we therefore include a quadratic term in the model.
Weeks Active To control for the life-cycle of a movie, we measure its country-specific
age by counting the number of weeks since the launch in a given country. The average
lifetime of a movie is some 6 weeks, but there are also some movies that run for more than
30 weeks (from which most are IMAX movies, the maximum is 299 weeks). We therefore
use the log in our models. Alternative specifications without this transformation, excluding
outliers, specifying a squared term, and including a weeks-active fixed effect do not change
the results.
Megaupload popularity Unfortunately, we do not observe a direct, movie-level mea-
sure of Megaupload/Megavideo usage. Using historical information about Google search
volumes, we can at least construct a country-specific time-variant measure of Megaupload
popularity (MP). From the Google Adwords Keyword Tool we obtain the monthly abso-
lute search volume of the keyword “Megaupload” as an average from April 2012 to March
2013 for each country. Google Trends then gives a time series of the search volume for the
same keyword scaled relative to the historical maximum in a specific country (see figure 1
for world-wide numbers). Using this information we can infer the absolute search volume
per country and month. Yearly data on the total number of fixed-line broadband sub-
scriptions provided by ITU allows to control for overall differences in internet usage across
countries.6 The final measure of MP is then given by the average monthly keyword search
volume divided by the number of broadband subscriptions per year. We set the variable to
the value of 2011 after the Megaupload shutdown. Figure 3 shows the average yearly MP
for Africa, Asia, Europe, Latin America, Oceania and the United States. It is important
to note that this is not a measure of actual Megaupload usage, but its popularity (among
users of Google, per broadband subscriber). It seems likely, however, that our measure is
highly correlated to actual usage.
For interpretational convenience we normalize this variable such that is bounded to
the interval [0,1] in the regressions. The mean is .19 with a median at .11.
6
We use broadband figures because movie files are typically too large to be transferred via dial-up connec-
tions in reasonable time.
105 Results
5.1 Descriptive Results
The left hand panel of figure 4 shows the development of (log) weekend revenues aggregated
over countries. The horizontal axis starts in July and ends in June to enable easy visual
comparison of values before and after the shutdown in January (indicated by the vertical
line). The connected dotted line to the right of the vertical line refers to the period to the
period of January to June 2012. Compared to the corresponding figures in other years (in
grey, 2011 is highlighted with diamonds), the graph suggests that the movies that ran in
the first half of 2012 performed less well than the movies in the first half of most of the
other years. The variance in the second half of the years (July to December) is higher,
but still the graph suggests that movies in 2012 performed less well than movies in other
years.
The right hand panel of figure 4 tells a similar story. The kernel density plot shows
that the distribution of revenues has a lower mode after the shutdown. In addition, the
left tail is slightly fatter, while there is no big difference in the right tail. This suggests
that movies that there were less average performing movies after the shutdown, while at
the same time there were more poorly performing movies.
A simple comparison of means suggests that average post shutdown revenues are some
12% lower (mean pre: 9.40, mean post: 9.28), a t-test suggests that this difference is
significant.
5.2 Model Results
Results of the main regressions are given in table 1. Across all columns we include year,
calendar week, country, and movie fixed effects. Standard errors are clustered on the
movie level to avoid issues caused by serial correlation.
The first column reports the baseline specification, including only the number of weeks
a movie has been active and the shutdown dummy. Patterns are similar across all columns.
The lifecycle follows the expected decreasing trend. The shutdown dummy is not signifi-
cantly different from zero. Hence, on average there seems to be no difference between the
11Figure 4: Box Office Revenues
10.5
.15
10
.1
9.5
.05
9
8.5
0
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun 0 2 4 6 8 10 12 14 16 18
Log Weekend Revenues, Over Time Log Weekend Revenues, Kernel Density
Mean over all countries, • 2012, 2011 Before Shutdown, After Shutdown
period before January 19th 2012 and after.
In column (2) we add the measure for movie size, explicitly modeling decreasing returns
to scale by including a quadratic term. The variable is measured in percentage units, i.e.
bounded between 0 and 1. A value of 1 indicates that a movie has the largest all-time
first-week audience in a given country. We find the expected non-linear relationship with a
maximum of 0.57. Surprisingly, the signs of the size coefficients change in the interaction
with the shutdown dummy. Hence, after the shutdown, revenues of movies that open
relatively small decrease, while only those of huge blockbusters increase.
Column (3) reports the results of a specification that controls for the yearly MP in a
given country. For interpretational convenience this variable is normalized to the interval
[0,1]. A value of 1 indicates that a country has the highest MP among all other countries
in a given year. It is important to note that a value of 0 does not mean that Megaupload
was not at all popular in a country, but that country has the lowest MP compared to all
other countries in our sample. The main effect is negative and significant at the 5% level.
The interaction with the shutdown dummy is also negative and significant at the 1% level.
The combination of both is finally reported in column (4). This is our preferred
specification. Compared to column (2), the pre- and post-shutdown size coefficients change
only marginally. The popularity coefficient is estimated less precise and the post-shutdown
popularity coefficient is about 50% smaller than in column (3). Those results imply an
insignificant average marginal shutdown effect of -.117 (standard error .093), the marginal
12Table 1: Fixed Effects Model Specification
(1) (2) (3) (4)
ln Weeks Active -1.559∗∗∗ -1.602∗∗∗ -1.559∗∗∗ -1.601∗∗∗
(0.016) (0.015) (0.016) (0.016)
Shutdown -0.030 0.098 0.105 0.220∗∗
(0.097) (0.092) (0.098) (0.097)
% First-Week Screens (S) 8.576∗∗∗ 8.564∗∗∗
(0.303) (0.302)
% First-Week Screens2 (S2 ) -7.515∗∗∗ -7.487∗∗∗
(0.383) (0.380)
Shutdown * S -2.542∗∗∗ -2.606∗∗∗
(0.414) (0.417)
Shutdown * S2 3.015∗∗∗ 3.067∗∗∗
(0.522) (0.527)
Megaupload Popularity (MP) -0.163∗ 0.018
(0.084) (0.075)
Shutdown * MP -0.478∗∗∗ -0.399∗∗∗
(0.083) (0.081)
Year Effects Yes Yes Yes Yes
Calendar Week Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Observations 331862 331862 331862 331862
R2 0.670 0.690 0.671 0.690
Dependent variable: Log Gross Weekend Revenues
∗ ∗∗
Note: Standard errors (clustered on movies) in parentheses, including movie fixed effects. p < 0.10,
p < 0.05, ∗∗∗ p < 0.01
shutdown effect at the mean is -.189 (.085) and significant at the 5% level.
Figure 5 illustrates the marginal effect of the shutdown according to the estimates
in column (4) of table 1. The plots show the marginal effect with corresponding 99%
confidence intervals at fixed values of MP. For comparison the overall distribution of movie
size is indicated in the background. Starting from the upper left panel, MP increases from
0 to 1. It should be noted that most observed values of MP are relatively low. The sample
distribution of MP is positively skewed, with a median of 0.10 (see figure A.5).
The striking result is that – almost independent of MP – the shutdown did not have
a significant effect on the revenues of a large majority of movies. Except for very large
13Figure 5: Marginal Effect of Shutdown as a Function of Movie Size – Table 1 (4)
15
15
1
1
10
10
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.5
0
0
5
5
−.5
−.5
−1
−1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0 Megaupload popularity fixed at M P = 0.1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
15
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1
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10
0
0
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−.5
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0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.25 Megaupload popularity fixed at M P = 0.5
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
15
15
1
1
.5
.5
10
10
0
0
5
5
−.5
−.5
−1
−1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.75 Megaupload popularity fixed at M P = 1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
values of MP (countries such as Bolivia, Chile, Kenya and Thailand), the coefficient for
very small movies is positive but insignificant. The marginal shutdown effect follows a
14u-shaped form in movie size that is only significant for medium-sized movies.7 With
increasing MP (towards the lower right panel of figure 5), the minimum moves south and
confidence bands expand. We only find a significant positive effect for huge blockbusters
in countries with a very low MP. Examples of such movies include Australia, Harry Potter
and the Half-Blood Prince, Ice Age: Dawn of the Dinosaurs Marvel’s The Avengers, and
The Hobbit: An Unexpected Journey in countries such as Australia, Denmark, Italy, Israel,
the Netherlands, and the United Arab Emirates.
5.3 Alternative Specifications
5.3.1 Measurement of Movie Size
It could be the case that our results largely depend on the way movie size is measured. The
problem with alternative measures such as absolute number of screens per country and
week, market share (in terms of screens) per country and week is that they are potentially
endogenous to the shutdown because theater owners can quickly adjust the number of
screens as a response to changes in demand. A measure that is theoretically related to %
First-Week Screens but very different from a measurement perspective is the production
budget. In our estimation sample, those variables do not show an overly high correlation
– the Pearson coefficient is 0.41. It is likely that there are some kind of decreasing returns
to scale, simply increasing production budget does not necessarily increase the number of
first-week screens. On top of that production budgets don’t vary across countries, while
first-week screens do, which allows us to implicitly control for different movie tastes in
different countries.
Columns (1) and (3) of table A.3 show the results of corresponding regressions. It must
be noted however, that the estimation sample is different in this specification. Unfortu-
nately, we can only observe production budgets for a subset of movies. This information
is mainly available for movies produced in the United States, i.e. many international pro-
ductions drop out. For easy comparison we also report results of corresponding models
with % First-Week Screens estimated on the same sample in columns (2) and (4).
7
This is also reflected in a model without the squared term (not reported here) where the size coefficient is
significantly negative.
15Because production budget is time-invariant the main effect cancels out in a movie
fixed effects model. In column (1), the interactions with the shutdown dummy have the
opposite sign as in the baseline model (column 2). Hence, we do not find a similar size
effect in this specification. The corresponding estimates in column (3) are similar. There
are two striking differences in this specification compared to column (4). First, the sign
of the interaction of the shutdown dummy and MP is positive and significant. Second,
the three-way interaction implies an inversely u-shaped, yet opposite size effect. However,
this is strongly opposing to the results obtained using the different size measure only at
first sight. The average marginal shutdown effect in this specification is 0.0001 with a
standard error of 0.192, the marginal shutdown effect at the mean is .069 (.141). This
again suggests that the box office revenues of a majority of movies in the sample did
not change in response to the Megaupload shutdown. The visualization of the marginal
shutdown effect in figure A.2 further underlines this. Dependent on the value of MP,
only movies larger than 30–80% of the observed maximum production budget experience
a significant increase in revenues, the effect is significantly negative for the largest 80%.
In sum, using production budgets as a measure of movie size can qualitatively confirm the
main results and add the interesting insight that there seem to be decreasing returns to
scale in the shutdown effect.
5.3.2 Sample Restriction
The relatively long sample period enables identification because we observe a large number
of different movies in different stages of the lifecycle in all countries. To ensure the results
are not driven by the long period of time in which also the popularity of Megaupload
follows an increasing trend, we estimated the models on various different subsamples.
Figure A.3 reports the coefficient of Shutdown*MP with corresponding 99% confidence
bands for a series of estimations similar to column (3) of table 1. The horizontal axis
indicates the starting date of the sample running until the 4th week of 2013. The point
estimate increases slightly with reduced sample size but remains remarkably stable. The
coefficient becomes insignificant when we reduce the sample to roughly half a year before
the shutdown. This seems plausible because in such a sample we observe too little movies
16that were unaffected by the shutdown, which makes a pre-/post comparison difficult.
5.3.3 Effect Persistence
It remains to explore whether the shutdown effect is only temporary or persists over time.
If the shutdown of Megaupload did not lead many users to stop consuming pirated content
online, but led them to substitute Megaupload with other suppliers of illegal downloads
and streams, we would expect to see that the development of movie revenues quickly
returns to the old equilibrium. On the other hand, if the shutdown led users to switch
to legal digital offerings or to substitute leisure time with something else than watching
movies, we would expect that the shutdown effect remains stable over time. This would
suggest that movie revenues are in a new, lower equilibrium after the shutdown. To test
this, we run a series of estimations similar to those reported in column (3) of table 1. The
idea is to specify a placebo shutdown at some date after the actual shutdown excluding
the time span from the actual to the placebo shutdown. As an example, if the placebo
shutdown is set to 2012w15, the estimation sample covers observations from 2007w31 to
2012w2 and 2012w16 to 2013w5. The horizontal axis indicates the date of the placebo
shutdown. The point estimates of Shutdown*MP are remarkably stable over time, showing
a decrease after the last quarter of 2012. The effect is significantly different throughout,
although it should be noted that the precision of course decreases with sample size.
5.3.4 Cross-Interactions
It is possible that the movie size effect is purely driven by some unobserved factor that
is unrelated to the shutdown of Megaupload but coincides in time for some unobserved
reason. This calls for looking at an interaction of size and MP. If signs and significance of
the three-way-interaction terms do not differ from that of the two-way interaction in the
baseline specification, we can rule out this explanation. Corresponding results are reported
in column (1) of table A.2. The estimates do not differ very much compared to the baseline
model. The coefficients of interest are Shutdown ∗ M P ∗ S and Shutdown ∗ M P ∗ S 2 .
The signs are equivalent to the corresponding two-way interactions. However, only the
quadratic interaction is significantly different from zero. This implies that the positive
17effect for blockbusters is more pronounced for higher values of MP as in the baseline
model. We can therefore rule out that the movie size effect is purely unrelated to the
Megaupload shutdown. The lower right panel of figure A.1 illustrates this by plotting the
marginal shutdown effect according to estimates in column (1) of table A.2.
5.3.5 General Downward Trend in Online Piracy
An alternative explanation for our results could be that the Megaupload shutdown coin-
cided with a general downward trend in online piracy due to the emergence of convenient
legal digital movie download/streaming services such as iTunes or Netflix. This would
lead our estimates to be biased downwards. If this is the case, we would expect to see a
decrease in the effect of other suppliers of pirated content on movie revenues as well. To
test this idea, we obtained data from Thepiratebay.se, one of the largest link portals for
BitTorrent. For every movie in our initial dataset (including country-specific titles) we
obtained all links listed on TPB along with the upload date. From this information we can
construct an indicator of whether a particular movie has been available on the BitTorrent
network from a given week onwards. We interact this variable with the Megaupload shut-
down dummy to test whether the correlation between BitTorrent availability and movie
revenues has changed after the shutdown. Of course this measure of piracy is likely to be
correlated with unobserved movie characteristics, which does not allow to make a strong
causal argument.
Results from an estimation on the same sample as the main regressions are reported in
table A.4. Column (1) shows results of a specification without movie fixed effects, instead
controlling for movie genre(s). The main effect is significant and positive, however this
estimate is likely to be biased upwards. Including a movie fixed effect in column (2) seems
to mitigate at least some of the endogeneity concerns. As expected, the main effect is
negative and significant in such a model specification. Most striking, however, is that the
interaction with the Megaupload shutdown dummy is not significantly different from zero
in either specification. This suggests that there was no general downward trend in the
availability of pirated content during and after the time of the shutdown of Megaupload.
186 Discussion and Conclusions
Our main finding is that smaller and larger movies were differentially affected by Megau-
pload’s shutdown: while only very large movies benefitted from the shutdown, revenue for
most smaller and medium-sized movies decreased with the shutdown.
This result is surprising for two reasons. First, one would not expect a decrease in
legal revenues after the shutdown. And second, it is not immediately clear why this effect
is especially strong for smaller movies but turns positive for larger movies.
We think a possible explanation for both results could result from information transfer
between customers. Let’s imagine two friends: user A only consumes legal content while
user B consumes legal and illegal content. Potential buyers are in turn influenced in their
consumption decision by two main sources of creating awareness: one way of influencing
consumers to go to a specific movie is to expose them with to a centralized marketing-
campaign. On the other hand, consumers are often also influenced through word-of-mouth
recommendations of friends or through social media. These word-of-mouth effects can be
transferred from consumers watching either legal or illegal content.
Figure 6 shows that both sources of awareness are actually driving consumers’ deci-
sions to watch a movie. Results from a representative panel of 25,000 German participants
indicate that the most influential sources of awareness such as TV advertisement or trail-
ers stem from the centralized marketing campaign, but word-of-mouth effects stemming
(recommendations from friends) are also an important source of awareness.
If the illegal content is made unavailable, user A does no longer receive recommenda-
tions based on user B’s illegal consumption. Then, if the displacement effect of B is larger
than the recommendation effect of A to B, shutdown of illegal content may reduce total
consumption.
We can also use this little thought experiment to give a possible explanation for the
different effects depending on movie size. Smaller movies usually have smaller marketing
campaigns, making word-of-mouth therefore a more important success driver. If some
of this word-of-mouth effect is then taken away with the shutdown of illegal content,
performance of smaller movies is likely to be hit harder.
A limitation of this paper is of course that we cannot test this mechanism. This would
19Figure 6: Sources of Awareness
0 5 10 15 20 25 30
TV advertisement
Trailers (seen in cinema)
Recommendation from friends
Posters, advertisement in the cinema
Online trailers
Reports and critics in newspapers
Radio advertising
Newspaper advertising
Reports and critics on TV
Online advertisement
Website of the cinema
Cinema program
Online reports and critics
Posters on the street
On the spur of the moment
Special promotion in the cinema
E-Mail advertisement
Other
“How do you decide to go to the movies?”
Data from representative sample of 25,000 German individuals older than 10 years (GfK Panel, 2011)
Source: German Federal Film Board (FFA), “Der Kinobesucher 2011”, p. 70
require micro-level data that allows to track individual behavior before and after the policy
intervention.
It remains to note that theatrical distribution of movies is a special setting because the
aggregate timing of adoption decisions is of crucial importance for the overall performance.
The cinema lifecycle of a movie is much shorter than in other distribution channels, such as
the homevideo market, rentals etc. Especially in the case of digital distribution a movie’s
life cycle is almost infinite because shelf space in digital stores is unlimited. This of course
renders timing and word-of-mouth much less important for aggregate sales.
We believe that our study offers an important implication for policy. When online
piracy has very different (even opposing) effects, interventions aiming at an reduction of
negative welfare effects are difficult to implement because of externalities that are able to
affect product variety and ultimately market structures.
We aim to contribute this alternative perspective to the emerging empirical literature
20on the effects of piracy. We believe that our setting offers a unique opportunity for causal
identification, which in combination with a rich data set that reflects a wide variety of
movies allows to investigate effect heterogeneity. Our results may also contribute to the
recent global debate on copyright in the digital society.
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22A Appendix
Table A.1: Countries
Frequency % Frequency %
Argentina 8207 2.47 Korea 7557 2.28
Australia 7166 2.16 Lebanon 3145 0.95
Austria 10927 3.29 Malaysia 1337 0.40
Belgium 12496 3.77 Mexico 11120 3.35
Brazil 9318 2.81 Netherlands 5678 1.71
Bulgaria 6469 1.95 New Zealand 9998 3.01
Chile 1079 0.33 Nigeria 1176 0.35
CIS (Russian Federation) 11301 3.41 Norway 6976 2.10
Colombia 4672 1.41 Peru 4784 1.44
Croatia 3639 1.10 Philippines 3538 1.07
Czech 5068 1.53 Poland 4277 1.29
Denmark 4343 1.31 Portugal 7692 2.32
Ecuador 1423 0.43 Serbia 6492 1.96
Egypt 3527 1.06 Singapore 3754 1.13
Finland 5398 1.63 Slovakia 2078 0.63
France 7889 2.38 South Africa 6134 1.85
Germany 13505 4.07 Spain 16438 4.95
Ghana 258 0.08 Sweden 6299 1.90
Greece 3471 1.05 Turkey 15133 4.56
Hongkong 6126 1.85 UAE 4552 1.37
Hungary 3178 0.96 UK 12438 3.75
Israel 2089 0.63 Ukraine 3723 1.12
Italy 10499 3.16 Uruguay 5884 1.77
Japan 4943 1.49 US 29529 8.90
Kenya 22 0.01 Venezuela 5117 1.54
Total 331862
23Table A.2: Fixed Effects Model Specification – Robustness Checks
(1)
ln Weeks Active -1.601∗∗∗ (0.015)
% First-Week Screens (S) 8.610∗∗∗ (0.297)
% First-Week Screens2 (S2 ) -6.825∗∗∗ (0.374)
Megaupload Popularity (MP) 0.089 (0.084)
MP * S 0.154 (0.600)
MP * S2 -3.876∗∗∗ (1.027)
Shutdown 0.206∗∗ (0.103)
Shutdown * S -2.446∗∗∗ (0.479)
Shutdown * S2 2.421∗∗∗ (0.579)
Shutdown * MP -0.290∗∗ (0.136)
Shutdown * MP * S -1.387 (1.011)
Shutdown * MP * S2 4.275∗∗∗ (1.429)
Year Effects Yes
Calendar Week Effects Yes
Country Effects Yes
Observations 331862
R2 0.690
Dependent variable: Log Gross Weekend Revenues
∗ ∗∗
Note: Standard errors (clustered on movies) in parentheses, including movie fixed effects. p < 0.10,
p < 0.05, ∗∗∗ p < 0.01
24Table A.3: Robustness Check: Production Budget as Size Measure
(1) (2) (3) (4)
ln Weeks Active -1.695∗∗∗ -1.719∗∗∗ -1.694∗∗∗ -1.719∗∗∗
(0.022) (0.022) (0.022) (0.022)
Megaupload Popularity (MP) 0.156∗∗ 0.204∗∗∗ -0.160 0.399∗∗∗
(0.068) (0.066) (0.471) (0.093)
Shutdown -1.337 0.311∗ -1.401 0.395∗∗
(2.112) (0.160) (2.645) (0.166)
Shutdown * ln Production Budget (B) 9.370 9.356
(6.078) (7.176)
Shutdown * ln Production Budget (B2 ) -9.101∗∗ -9.003∗
(4.352) (4.907)
Shutdown * MP -0.482∗∗∗ -0.302∗∗∗ -0.504 -0.522∗∗∗
(0.081) (0.079) (2.930) (0.191)
% First-Week Screens (S) 4.426∗∗∗ 4.881∗∗∗
(0.276) (0.383)
% First-Week Screens2 (S2 ) -3.747∗∗∗ -3.687∗∗∗
(0.290) (0.471)
Shutdown * S -2.177∗∗∗ -2.961∗∗∗
(0.440) (0.549)
Shutdown * S2 2.702∗∗∗ 3.396∗∗∗
(0.541) (0.698)
MP * B 1.526
(1.253)
MP * B2 -1.365
(0.884)
Shutdown * MP * B 0.685
(7.293)
Shutdown * MP * B2 -0.758
(4.500)
MP * S -1.224∗∗
(0.615)
MP * S2 0.078
(0.859)
Shutdown * MP * S 2.157∗∗
(1.072)
Shutdown * MP * S2 -2.240∗
(1.256)
Year Effects Yes Yes Yes Yes
Calendar Week Effects Yes Yes Yes Yes
Country Effects Yes Yes Yes Yes
Observations 120503 120503 120503 120503
R2 0.727 0.733 0.727 0.733
Dependent variable: Log Gross Weekend Revenues
∗ ∗∗
Note: Standard errors (clustered on movies) in parentheses, including movie fixed effects. p < 0.10,
p < 0.05, ∗∗∗ p < 0.01
25Figure A.1: Marginal Shutdown Effect wrt. Movie Size – Table A.2 (1)
15
15
1
1
.5
.5
10
10
0
0
5
5
−.5
−.5
−1
−1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0 Megaupload popularity fixed at M P = 0.1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
15
15
1
10
10
.5
1
.5
0
5
5
0
−.5
−.5
−1
−1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.25 Megaupload popularity fixed at M P = 0.5
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
15
15
10
10
1
1
.5
5
5
.5
0
0
−.5
−1 −.5
−1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.75 Megaupload popularity fixed at M P = 1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of % First-Week-Screens Overall Distribution of % First-Week-Screens
26Figure A.2: Marginal Shutdown Effect wrt. Production Budget – Table A.3 (3)
5
5
−1−.50 .5 1
−1−.50 .5 1
4
4
3
3
2
2
1
1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0 Megaupload popularity fixed at M P = 0.1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of ln Production Budget Overall Distribution of ln Production Budget
5
5
4
4
−1−.50 .5 1
−1−.50 .5 1
3
3
2
2
1
1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.25 Megaupload popularity fixed at M P = 0.5
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of ln Production Budget Overall Distribution of ln Production Budget
5
5
4
4
−1−.50 .5 1
−1−.50 .5 1
3
3
2
2
1
1
0
0
0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Megaupload popularity fixed at M P = 0.75 Megaupload popularity fixed at M P = 1
Marginal Effect of Shutdown, 99% CI Marginal Effect of Shutdown, 99% CI
Overall Distribution of ln Production Budget Overall Distribution of ln Production Budget
27Figure A.3: Sample Restriction Figure A.4: Effect Persistance
2
2
1.5
1.5
1
1
.5
.5
0
0
−.5
−.5
−1
−1
−1.5
−1.5
−2
−2
07−31
07−41
07−51
08−09
08−19
08−29
08−39
08−49
09−07
09−17
09−27
09−37
09−47
10−05
10−15
10−25
10−35
10−45
11−03
11−13
11−23
11−33
11−43
12−01
12−03
12−05
12−07
12−09
12−11
12−13
12−15
12−17
12−19
12−21
12−23
12−25
12−27
12−29
12−31
12−33
12−35
12−37
12−39
12−41
12−43
12−45
12−47
12−49
12−51
13−01
13−03
Moving Towards the Shutdown Moving Away from the Shutdown
Coefficient Shutdown * MP, Coefficient Shutdown * MP,
95% Confidence Interval 95% Confidence Interval
Figure A.5: Sample Distribution of Megaupload Popularity
6
4
2
0
0 .2 .4 .6 .8 1
Megaupload Popularity per Country and Year, Normalized
28Table A.4: Robustness Check: BitTorrent
(1) (2)
No Movie Effects Movie Effects
ln Weeks Active -1.247∗∗∗ (0.028) -1.557∗∗∗ (0.016)
Torrent Available 0.370∗∗∗ (0.054) -0.278∗∗∗ (0.062)
Shutdown -0.012 (0.165) -0.020 (0.120)
Shutdown * Torrent Available -0.025 (0.095) -0.016 (0.090)
Year effects Yes Yes
Calendar week effects Yes Yes
Country effects Yes Yes
Genre effects Yes No
Observations 331862 331862
R2 0.406 0.671
Dependent variable: Log Gross Weekend Revenues
∗ ∗∗
Note: Standard errors (clustered on movies) in parentheses, including movie fixed effects. p < 0.10,
p < 0.05, ∗∗∗ p < 0.01
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