Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

書籍海賊版の売り上げへの影響：日本のマンガをケースとして
田中辰雄

2016 年 12 月 29 日
DP2016-027
https://ies.keio.ac.jp/publications/7121/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
29 December, 2016

書籍海賊版の売り上げへの影響：日本のマンガをケースとして
田中辰雄

IES Keio DP 2016-027
2016 年 12 月 29 日
JEL Classification: D12; L82; M3; O34
キーワード: 著作権; 海賊版; 電子書籍; 差の差; マンガ【要旨】
インターネット上の書籍の海賊版が正規版の売上に与える影響を、日本の漫画を事例として分析した。海賊版の流布程度を個別タイトル単位で調査し、それが正規版の売上に与える影響をみるとともに、出版社が共同で行った海賊版ファイルの削除作業を自然実験として、差の差による分析も試みた。二つの分析方法の結果は一致しており、次の事実が確認できた。海賊版は現在連載中の作品、特にその最新版の売り上げを減少させる。その一方、すでに完結した作品の売り上げを増加させる。前者の効果は海賊版が正規版を置き換える通常の被害である。後者の効果は、海賊版が、宣伝されずに忘れさられている過去作品を読者に思い出させる効果（remind effect)と考えられる。

田中辰雄
慶應義塾大学経済学部〒108-8345
東京都港区三田2-15-45
tatsuo@econ.keio.ac.jp

The Effects of Internet Book Piracy: The Case of Japanese
ComicsFirst version: October 18, 2016
Tatsuo TANAKA
Faculty of Economics, Keio University
tatsuo@econ.keio.ac.jp

Abstract
In this study, the effects of internet book piracy in the case of the Japanese comicbook market were examined using direct measurement of product level piracy ratio anda massive deletion project as a natural experiment. Panel regression and difference-indifference analysis consistently indicated that the effect of piracy is heterogeneous:piracy decreased the legitimate sales of ongoing comics, whereas the legitimate sales ofcompleted comics increased. The latter result is interpreted as follows: piracy reminds
consumers of past comics and stimulates sales in that market.
Keywords: copyright, piracy, e-book, comic, Japan, difference-in-difference
JEL: D12,L82,M3,O34

1

I Introduction
Whether piracy reduces legitimate sales has been a hot issue since the internet
became the common infrastructure and provided users with new piracy options such as
file sharing. Natural intuition tells us that piracy displaces legitimate sales because
consumers are entertained by the same product without paying a fee. However, if
piracy has some kind of advertisement effect such as sampling, network effect, or
market-enlarging through word-of-mouth, and that overrides the displacement effect,
legitimate sales may not be decreased (Takeyama, 1994; Peitz & Waelbroeck, 2008).
Since copyright theory indicates that both the displacement effect and advertisement
effect are possible, the question is left for empirical analysis.
There have been many econometric studies on the impact of piracy in the recent two
decades with respect to the music and motion picture industries. Accumulated studies
are surveyed in Liebowitz (2006), Oberholzer and Strumpf (2010), and Danaher, Smith
and Telang (2013). However, there are very limited econometric studies on piracy in thebook industry. One of the reasons is that piracy of books has been limited comparedwith music and movies because digitization of books lagged behind music and movies.
Additionally, e-book readers such as Kindles are specialized devices, not general
purpose devices like PCs, making them relatively free from piracy. Piracy can besuppressed on specialized devices more easily due to the limited software working on it.
Nevertheless, digitization of books has momentum in this decade to push up the shareof e-books to over 40% of publishers’ total net revenue in the US and over 10% of salesin Japan in 2014.1 As people get accustomed to reading e-books on various devices,
including general purpose devices such as PCs or smartphones, piracy of books may
become a serious concern for publishers in the near future.
The intent of this paper is to examine the displacement and/or advertisement effectof piracy in the case of comics in the Japanese book industry. There are three reasons
we chose comics in Japan as a research target for book piracy. Firstly, piracy in comics
is already very common compared with other book categories. One can find pirated
illegal copies of almost all popular comics on the internet, and, as soon as a new volume
is released from publishers, a pirated copy is uploaded onto piracy sites very quickly.
Japanese comic publishers belong to an anti-piracy association named CODA (Content
Overseas Distribution Association), which insists that loss caused by overseas piracy isestimated to be double the overseas legal revenue from 2014.
1

Source: American Association of Publishers for US and Zenkoku Shuppan Kyoukai for Japan2

Secondly, CODA carried out a massive deletion project from July of 2015 till Marchof 2016, which provided us with a natural experiment to test the impact of piracy.
Publishers chose some of their comic titles as the target of this project, and CODA sent
repeated warnings to piracy sites to delete all illegal files from July of 2015. Thismeasure resulted in the decrease of illegal files of target comics to a small but
significant extent. If we compare the sales of target comics (treatment group) with nontarget comics (control group), we can evaluate the effect of this deletion project. In
other words, we can apply difference-in-difference method to the comics market piracy
case.
Thirdly, Japanese comics consist of multiple volumes, which reduces theendogeneity problem. Since the early volumes were published several years ago,
consumers know the style and quality of the comic very well. Thus, we can assume thatthe popularity of the comics is established and fixed during the research periods. If thisassumption holds, fixed effect term of panel regression removes the effect of the comics'
popularity, which is the source of the endogeneity problem annoying piracy
researchers.
We measured piracy ratio by the number of piracy sites that have access to the
comics. We counted the number of piracy sites with respect to 3,360 volumes of 484
comic titles for eight months and applied ordinary panel regression and a difference-indifference model. Both approaches consistently showed that the effects of piracy on
legitimate sales are heterogeneous depending on whether the comics are ongoing orcompleted. Piracy decreases sales of ongoing comics, but it increases sales of completed
comics. To put this another way, displacement effect is dominant for ongoing comics,
and advertisement effect is dominant for completed comics. Since completed comics
series have already ended, and publishers no longer do any promotion for them,consumers almost forget completed comics. We can interpret that piracy reminds
consumers of past comics and stimulates sales.
The remainder of the paper is organized as follows. Section II contains a briefsummary of the literature of studies on piracy, and, in section III, the Japanese comic
industry is described. In sections IV and V, we explain the data collection procedureand the model specifications, respectively. Section VI contains a report of the results ofpanel regression using direct measurement of piracy, while section VII features theresult of difference-in-difference analysis. Section VIII consists of the summary anddiscussion.

3

II Literature
Although early studies on the effects of piracy exploited internet penetration ratio asa proxy of piracy (Liebowitz, 2008; Zentner, 2005), this ratio is not a good measure ofpiracy because internet usage shifts consumer demand from old style entertainment
media such as CDs or DVDs to new style media such as online music services with
fixed fees, YouTube videos, smartphone applications, SNS chatting, basic web surfing
etc. Therefore, CDs and DVDs are crowded out because of consumers' budget and time
constraints, and sales may decrease even if there is no piracy.
A more correct approach is to measure each product's piracy ratio directly and lookat the correlation between piracy and sales. In this approach, endogeneity is a key
issue to be solved because, when content becomes popular, both sales and piracy
simultaneously increase, resulting in a spurious positive correlation between sales andpiracy. Another improved approach is to exploit an exogenous shock as a naturalexperiment to estimate the causality of piracy. If an exogenous shock, such as legal
enforcement, occurs to part of the sample, we can compare the consequences between
the sample with the shock (treatment) and the sample without the shock (control).
The results of these two approaches, unfortunately, seem to be mixed so far. Studies
using a product level piracy ratio tend to indicate that piracy does not reduce
legitimate sales. Oberholzer-Gee and Strumpf (2007) exploited file sharing data from a
P2P server and found that piracy did not reduce music CD sales, using a Germanschool holiday as an instrument variable. Martikainen (2011), Smith and Telang(2009), and Tanaka (2004) also measured the piracy ratio directly and reported thatpiracy did not harm legitimate sales.
Studies using exogenous shock as a natural experiment, on the other hand, tend toindicate that piracy is harmful to legitimate sales. Danaher, Smith, Telang, and Chen
(2014) reported that French anti-piracy law, Hadopi (the so-called three strikes law),
increased French iTunes music sales by 22-25%. This was shown via difference-indifference method using music sales of other EU countries as the control variables.
Adermon and Liang (2014) reported similar results in the case of Sweden's copyright
law enforcement in 2009, and Danaher and Smith (2014) found the revenue of major
movie studios increased after the shutdown of famous cyber locker site, MegaUpload.
These natural experiments suggest the displacement effect is dominant against piracy
on the internet.
In the case of the book industry, to our knowledge, there is no study that either
directly measures piracy ratio or exploits a natural experiment. One excellent
4econometric study is a field experiment done by Hardy, Krawczyk, and Tyrowicz (2014).
They chose 94 pairs of similar books, from which one was randomly assigned to be thetreatment and the other the control. They intensively deleted unauthorized copies oftreatment books during one year, leaving control books to be pirated freely. Comparisonof the sales of pairs showed that removal of unauthorized copies had no bearing on
legitimate sales. Other literature on the effects of book piracy on the internet is
anecdotal. For example, Hilton (2011) reported ten weeks free exposure of eight books
online resulted in a 26% increase in legal sales. O’Leary (2009) also claimed there waslittle connection between file sharing and book sales after putting eight books on free
distribution. In spite of this anecdotal evidence, in each case the sample size was too
small to evaluate statisticallyThis study is the first to examine the effect of internet piracy on the book industry
by using a large sample — albeit one that is limited to the specific category of comic
books — and applying both a direct measuring approach and a natural experimentapproach. We obtained consistent results with these two approaches, which have often
shown opposite results in the case of the music and movie industries.

III Industry
Industry
The Japanese comics industry consists of comic books and comic magazines, thetotal sales of which were 4.046 billion dollars in 2014. Of these two categories, we focus
on comic books, whose corresponding category in the US is comic and graphic novels.
The size of the comic book market of these two countres is shown in Table 1 forcomparison. In 2014, the total sale of comic books in Japan was 2.853 billion dollars —almost three times more than US sales of 0.935 billion. When we consider thedifference of total book market size between Japan and the US, the difference in comics
market size is impressive. If we divide the sales of comics by total book market size, wefind that the share of comics in the total book market is 36.1% in Japan, as comparedto 2.6% in the US. This means the size of the comics market in Japan is, relatively
speaking, over ten times bigger than its counterpart in the US. In Japan,
approximately one third of all books are comics. When we divide the sales of comics bytotal population, we obtain per capita expenditure on comics. This figure amounted to$2.90/person in the US and $22.40/person in Japan. The latter number increases to$31.80/person if we include comic magazines. This implies that the propensity ofbuying comics is ten times higher in Japan than in the US. Comics (called "manga" inJapan) is a major category in the Japanese book industry.
5

Table 1 Size of comics market in Japan and the US (2014)

Japan
US

comic
books

all books

share ofcomicsexpenditure
per capita(million$)(million$)(%)($/person)

2,853
935

7,898
35,99536.1%
2.6%

22.4 (31.8)*
2.9(excluding magazines)* when magazine is included
source: ICv2.com
Annual Report and 2014 Global Publishing Statistics, International Publishers Association,monthly report of publications, Zenkoku Shuppan Kyoukai, JapanThe market structure of the comics publishing industry is oligopoly, both in the US
and Japan. In the US, two dominant publishers, Marvel Comics and DC Comics, had
100% share in the top 20 ranking and 96% of top 50 ranking in July 2016. There are
four major publishers in Japan: Shuei-sha, Kodan-sha, Shougakkan, and KADOKAWA,
which held 98% of the top 20 comics ranking in July 2016. (Ranking data is based on
ICv2.com for the US and ORICON for Japan).
Figure 1 shows the sale of comics in Japan from 1993 to 2015. Paper comics peaked
at 2,365 million dollars in 2005 and have gradually declined to 1,911 million dollars in
2015. Some publishers claimed that this decline was caused at least partly by piracy.
However, since sales of electronic comics started to increase from 31 million dollars in2005 to 1,045 million dollars in 2015, the sales decline of paper comics may be due tojust a demand shift from paper to electronics, as is often the case in the digital age.
Whether comic piracy harms legal sales or not is still an empirical question.

6

Figure 1

Sales of Comics in Japan (unit=million$)magazines is not included, 1$=110yen
2365

2500

2000

Paper comics
1911

1500

1045
1000

Electronic comics

500

31
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015

0

paper comics

electric comics

Source：monthly report of publications, Zenkoku Shuppan Kyoukai, Japan

Unlike the case of the music industry, the main mode of comic piracy is not filesharing, but piracy sites, which consist of cyber locker sites and reach sites. Illegal files
are uploaded to the server of cyber locker sites, and reach sites provide users with asearch service to find links to the files. Users search for a comic name on the reach siteand obtain the link to the file on the cyber locker site. Then, they download the pirated
comics from the cyber locker sites. Cyber locker and reach sites are managed bydifferent firms.
The main reason for this division of labor is to avoid legal prosecution. It is illegal tokeep pirated files on a server when it is known that they are pirated files. To put it
another way, if the individuals who run the cyber locker site are unaware that the fileis pirated, they do not face legal punishment. When a copyright holder requests thecyber locker site delete the file, the site administrator must do so because, at that time,
he has learned that it is an illegal file (so-called “notice-and-takedown” policy). Reachsites have no legal obligation because they list only URL links. If the two sites were
integrated, it would be clear that the administrator of the integrated site keeps the fileon the server with the knowledge that it is illegal, because the site provides a comic
search service. Thus, the integrated site would be shut down.

7

IV Data
In July of 2015, the Content Overseas Distribution Association in Japan (CODA)
started the Manga Anime Guardian Project, which requested cyber locker sites todelete the pirated comics files. This was a large project funded by the Ministry of
Economy, Trade, and Industry, and most comic publishers (22 publishers) in Japanjoined it. In this study, we used the data of four major publishers, Shuei-sha, Kodansha, Shougakkan, and KADOKAWA, whose total share in the top 30 comic sales inannual ranking during 2010-2014 was almost 95%.
Deleting pirated files is a cat-and-mouse game since deleted files are soon restored.
However, this project still had some effect because it covered many publishers and sent
requests repeatedly. The actual requesting operation was carried out by a firm hired by
CODA, which crawled webs to find pirated files and sent warning messages
continuously until the files were deleted. Reportedly, over 90% of piracy files weredeleted by these requests, though new files were soon restored with new URLs. In
section VII, we show via difference-in-difference method that this project had a small
but significant effect on piracy level.
As mentioned in the previous section, there are two types of piracy sites: reach sites
and cyber locker sites. Cyber locker sites were the target of this deletion project. For
the purpose of our research, however, reach sites were more important than cyberlocker sites because, from the view of users, reach sites are the places for they findpirated comics. Let us assume that one cyber locker site is shut down, but all reachsites keep active links to pirated files. This assumption is reasonable because reachsites usually list links to multiple locker sites, and/or they can add a new link toanother cyber locker site. If the reach sites maintain active links, there is no change tousers because they can access pirated files as before. However, if one reach site is shut
down, it causes an inconvenience to the users of the site because they have to findanother reach site to access pirated comics. Therefore, we focused on the activities ofreach sites in our research.
Unfortunately, the number of downloads of each comic title is not open to the
public. Thus, we had to use a proxy of the degree of piracy for each comic. For thispurpose, we used the number of reach sites that had active links. For example, when
there were ten reach sites with active links for comic A and five reach sites for comicB, we assumed that comic A was more pirated than comic B because more users wereable to find the link to the pirated files of comic A than comic B. Hardy, Krawczyk, andTyrowicz (2014) also used number of available piracy files on the internet as a proxy of
8the degree of piracy.
There are many reach sites on the internet. Of these reach sites, we chose 16 major
Japanese active reach sites to be researched. "Active" means that the links have been
updated recently. "Japanese" means that the pirated comic files have not beentranslated to non-Japanese language. Since the Manga Anime Guardian Projecttargeted Japanese sites only, we also limited our research scope to the Japanese site.
These 16 Japanese sites are "major" sites in the sense that the pirated files are wellmanaged by the administrators and the sites keep complete volumes of many comics,
whereas other minor sites are not managed by administrators and often lack complete
volumes. The largest reach site of our 16 sites covers 81% of all targeted comics, andthe smallest site covers 21%. Since coverage of other minor sites is probably below 20%,
we suppose 16 reach sites cover most piracy users.
Almost all comics series consist of multiple volumes. Of these volumes, we chose thefirst, middle, and most recent two volumes as samples. If the comic consists of ten
volumes, for example, we count pirate links of volume 1, 5, 9, and 10, respectively. The
reason for this choice is that readers of these volumes have different characteristics.
The readers who buy recent volumes are more likely fans of the comic and havepurchased previous volumes already, whereas the ones who buy the first or middle
volume have noticed the comic recently and started reading. This heterogeneity may
influence the effects of piracy. In order to take this heterogeneity into consideration, we
count the number of reach sites where the first, middle, and most recent two volumes
were linked.
Both sales and number of reach sites are monthly data. Sales data starts in Marchof 2015 and ends in February of 2016. Counting the number of reach sites began inJune of 2015, a month before the massive deletion project started, and ended in
January of 2016.
To make the data structure clear, we show the hypothetical case of a comic in Table
2. This comic is assumed to consist of ten volumes, volumes 9 and 10 of which were
published in August and December of 2015, respectively. Volumes 1 and 5 were
published several years ago. Distribution of sales of newly published volumes 9 and 10
was skewed because sales were highly concentrated to the initial few months. The
number of reach sites that had active links to volume 1 and 5 slightly decreased fromsix sites in June to approximately five sites after July, thanks to the massive deletionproject. The decrease in the number of reach sites was small because of the cat-andmouse game structure of deletion. Regarding volume 9, links to pirated files do notappear on the reach site in the published month of August, but they appear on five9

sites a month later. As for volume 10, three reach sites began listing links in the
publishing month. This paper's research question is whether the number of reach sites
reduced comic sales and whether the massive deletion project increased sales.

Table 2 Data Structure (example)

year
month
vol1
vol5
sale
vol9
vol10
number vol1
vol5
ofreach vol9
vol10
sites

3
100
100

4
100
100

5
100
100

6
100
100

2015
7
8
9
100 400 200
100 300 200
4000 1000

6
6

5
5

5
5
0

4
4
5

2016
12
1
2
500 200 100
300 100 100
400 400 400
5000 1500 1000
5
6
5
6
6
5
6
6
5
3
5

10
11
100 100
100 100
800 5005
5
5↑deletion start in July↑counting start in June

Covered in this research are comics published by four major publishers (Shuei-sha,
Kodan-sha, Shogakkan, and KADOKAWA). These four publishers listed 322 comic
titles as the targets of the massive deletion project. As control data for difference-indifference regression, we added another 162 comics published by these four publishers
that were not the target of the massive deletion project. Therefore, the total number ofcomics we counted on the reach sites was 484 (=322+162). Almost 70% of comics are
digitized; therefore, sales data of the electronic versions was also available. We treatedsales of the paper version and electronic version separately because electronic books
and paper books are not interchangeable (Hu & Smith, 2013), and substitutability withpirated files may be different between electronic and paper comics. Adding the paper
versions and electronic versions together, the total sales data to be analyzed increased
to 840, 551 of which belonged to the treatment group and 289 of which belonged to thecontrol group. Since we chose four volumes from each comic, the total number ofvolumes was 840*4=3360, which is the maximum number of groups of the panel
regression. With approximately ten months of data for each volume, as shown in Table
1, the total data size was over 30,000.
Of our 840 comics, 601 were ongoing comics in the research periods, meaning thatthey ended in 2015 or continued into 2016. The remaining 239 were completed comics
that lasted until 2014. These categories, ongoing and completed, are important in this10

paper.
Counting the number of reach sites was done manually rather than via web
scraping software because URL expression of links on reach sites changed over time,
and they sometimes became inactive. As a result of the massive deletion project, piracy
files on the cyber locker sites were frequently deleted and soon restored. Therefore, the
URL links on the reach sites often became inactive or expressed differently. To obtainaccurate information, we visited every reach site monthly and checked to see if thelinks were active or not by clicking the URLs and exiting just before the download
process began. In other words, we checked 16 reach sites every month with regards to3,360 volumes.
Book sales data is not shipping-based but actual sales based which was offered by
the four publishers.

V Model Specifications
Specifications
We used two models to examine the effect of piracy on legal sales. The first wasordinary panel regression, which correlated comic sales to the number of reach sites
controlling the comics' popularity as fixed effect.
log(Yit) = ai+ b*log(Siteit+1) +c*Zit + eit(1)

Yit represents the sales of comics i in month t, whose unit is number of books. Siteit is
the number of reach sites that have active links to comic i in month t. Log
transformation was applied assuming nonlinear effect of reach site on the sales. Zit is avector of other exogenous variables that affect the sales of comics, such as media mix,
TV promotion, animation etc. The error term eit is an idiosyncratic random variable.
The coefficient b represents the effect of piracy — either displacement effect (b<0) or
advertisement effect (b>=0).
The popularity of individual comics, which is a source of endogeneity, was assumed
to be controlled by fixed effect ai. This assumption is valid if popularity was constantwithin the research periods. The assumption that popularity did not change during the
research periods seems unrealistic and misleading when compared with the musicindustry studies. However, unlike the music industry, there were two reasons to adopt
this assumption in our research. First, in the case of comics, the quality of product was
already known to consumers because all comics consisted of multiple volumes, and theearly volumes were published several years ago. In the case of the music industry, the11quality of CD albums is not known to consumers at the time when the albums are justreleased. Since the quality of CD albums changes from album to album even if theartist is the same, consumers gather information about the quality of the album
through reviews in music magazines, comments on music web sites, word-of-mouth,
trial listening etc. If consumers find the quality of the album to be excellent, they begin
to purchase CDs or download its pirated files. Thus, in the music industry studies, CD
sales and downloading piracy naturally increased simultaneously in the research
periods, which inevitably caused an endogeneity problem. That is the reason why moststudies on music CD piracy have exploited instrumental variables.
However, in the comic case, early volumes were published several years ago. The
median starting year in our data is 2009. Thus, half of the comics in our study started
over six years ago. Seventy five percent of comics started before 2012, and 95% started
at least one year prior to the research period. What this means is that information
about the comics such as characters, storyline, and reviews are already open to public.
Consumers can make their subjective evaluations of the comic before publication of
new volumes. In other words, consumers know the quality of the comic already because
each volume is a part of the same content that started years before. Hence, it is areasonable assumption that the popularity of the comics was established and constantduring the research periods. The main factors varying the sales with time in the
research periods were time trends and promotions like TV animation or electronic
promotion, which were represented by controlled variable Z.
The second reason that we assumed fixed effect could control endogeneity is thatthe explanatory variable, the number of reach sites, is determined by site
administrators and not by consumers. In case of music piracy, both the number ofdownloads and CD sales are determined by the same consumer; thus, correlation
between them is almost certain and exactly simultaneous. In our research, theexplanatory variable was not the number of downloads but the number of sites withactive links to the comic, and whether reach sites list links to the comic or not isdetermined by site administrators who do not have information about the changing
popularity of the comic among consumers.
How can administrators simultaneously learn about a change in the popularity ofcomics? Although product price is a signal of demand change in the ordinary market
transaction, there is no price of piracy. The price of paper books cannot be a signal
because the retail book price is fixed by publishers in Japan, owing to a resale pricemaintenance system. Comic ranking in ORICON is useless because it shows only the
top 50 sales weekly; hence, sales of the remaining titles, which make up over 90% of
12the total, are unknown. If the reach site already had the link to the comic, theadministrator could learn the change in popularity by looking at the consumer click
count of the link. However, since we are discussing whether the administrator lists thelink on the site or not, the site does not have the link yet. There is no systematic way
for administrators to simultaneously know the change in popularity of comics they
have not listed yet. Therefore, there will be considerable lag for administrators to learn
the change in popularity and add new links to their reach sites, which makesimultaneous correlations between changes in popularity and the number of reach sites
very low or almost zero. If popularity change does not correlate with explanatory
variables, we can include it in the error term eit without endogeneity bias.
Considering these two reasons, it is not an unrealistic assumption that fixed effectai can control the endogeneity of the popularity of comics. Therefore, as the first
approach, we adopted the ordinary panel regression, assuming that fixed effect ai
controlled the popularity of the comic.
Although the assumption of constant popularity might be probable, it is notcertain. Changes in ongoing comics such as a shift in storyline or the death of a popularcharacter can affect sales even within a research period. Also, exogenous shocks otherthan Z (tv animation or promotion) impact popularity. For example, if a big sports
event such as the summer Olympics is held in the research periods, sports comics may
be sold more during the event periods, and administrators may notice this change andquickly added new related links to their sites. Thus, popularity can change during a
research period to some extent, which causes an endogeneity problem.
To cope with this problem, as a second approach, we applied standard differencein-difference regression using the massive deletion project that started in July 2015 asa natural experiment.
Log(Yit) = ai+ b0*DafterJulyt + b*Deletioni*DafterJulyt +c*Zit + eit(2)

DafterJulyt is a dummy variable equal to one if t is after July 2015, and Deletioni is adummy variable equal to one if comic i is a target of the massive deletion project. If
coefficient b is positive, we can say that the massive deletion project increased the salesof comics because targeted comics were sold more than non-targeted comics after thedeletion project, which suggests that piracy decreased legal sales. This regression cancontrol the endogeneity problem because it does not use number of reach sites as an
explanatory variable.
To apply this approach, two key variables, DafterJulyt and Deletioni, had to be13

randomly chosen. We can suppose that DafterJuly was randomly chosen because Julywas selected by the technical parameters of this project, and most consumers were
unfamiliar with this project. , However, Deletioni was not randomly chosen because thetarget comic list of this project was offered by publishers. It is likely that publishers
chose popular comics or comics that they wanted to promote for specific reasons. In
order to address this bias, we had to choose control comics with similar characteristics
to the target comics. We examine this similarity later.
As other exogenous variables Zit, we adopted the following six variables and monthly
dummies.

timet: Since all entertainment content gradually loses popularity after the initialboost, sales of comics also tend to decrease slowly with time. The variable time,
therefore, is a trend variable starting from one to twelve during the research periods
in order to remove this general tendency.

log(trendit): Sales distribution of newly published volumes is quite different fromother volumes because sales in the first month are typically extremely high andthen decrease very rapidly. To represent this skewed distribution, we adopted thevariable log(trendit) , trend, which starts from one at the month when the volume
was published in the research periods. Log transformation was done to present theskewed pattern of declining sale. This variable was applied to volumes published in
the research periods — that is, the recent two volumes of ongoing comics.

newpub1it, newpub2it: When a new volume is published, sales of the first and middle
volumes also increase because of the advertisement effect of the new volume. These
two variables are dummy variables for this advertisement effect, equal to one after
the most recent volumes of the comic are published. Newpub1 is equal to one after
for the second recent volume, and newpub2 is for the most recent volume. Thesevariables were applied to ongoing comics only because completed comics do not have
newly published volumes.

animeit: When the comic becomes an animated TV series, the sale of the comic bookgains momentum. Variable animeit is equal to one if the TV animation series of the
comic i was on air at the month t.

e_promotionit: As for the electronic version of the comic, sales heavily depend on
promotion by the electronic shop sites. If the electronic shops put the comic's
advertisement on the banner of the top page, the comic sells well. Also, electronic
shops sometimes set several comics' prices down by 50% or more since the marginal
cost of supplying the electronic files is almost zero, resulting in a boost in sales.
14E_promotionit is a dummy variable equal to one if the comic was advertised or
discounted on the electronic shopping sites.

Monthly dummiest: Monthly dummies were included to remove seasonal variationand temporary monthly common shocks.
Price was not included as a control variable because book price is fixed by publishers
in Japan. Retail book stores in Japan are not allowed to change book retail prices; thus,
book prices did not change in the research period, with the exception of promotion inelectronic shops. The expected sign was negative for "time" and "ltrend", and positivefor "anime", "newpub1", "newpub2", and "e_promotion".
Descriptive statistics are shown in Table 3. Mean sales were 1,165 books/month,which was far different from their median 70 books/month, indicating that distributionof sales was skewed (skewness = 41.75). Log-transformation of sales made the mean
and median almost the same number, 4.2, and the distribution became not skewed(skewness = 0.106). The mean number of reach sites was 7.163, with a distribution
similar to normal distribution except for many zero points. One fourth of the number ofsites were zero because it takes some time for new links to appear on the reach sites
after a new volume is published. In that case, the number of sites continues to be zero
until pirated files are uploaded and links created.
Table 3 Descriptive Statistics
Variable
unit
sale
#
log(sale)
site
#
time
ltrendnewpub1
dummy
newpub2
dummy
anime
dummy
e_promotion dummy

Obs
31167
31167
26880
40320
14021
20160
20160
31167
31167

Mean
1165
4.202
7.163
6.5
0.977
0.500
0.322
0.041
0.036

Median Std Dev
70
11353
4.263
2.321
8
5.083
6.5
3.452
1.099
0.920
1
0.500
0
0.467
0
0.198
0
0.186

Min
0
0
0
1
0
0
0
0
0

Max
99982613.815
16
12
2.565
1
1
1
1

VI Panel regression
Table 4 shows the result of the panel regression of model (1). Column (1) in Table 2
uses all comics. Before examining the effect of reach sites, let us see other control
variables to check the validity of the estimation. Coefficients of time and log(trend)
were negative, as expected, which indicates that initial sales rapidly decreased at a
rate of power t-1.242 for newly published volumes and gradually declined at 3.32% per15month after the initial boosts. Other exogenous variables, newpub1, newpub2, anime,and e_promotion were positive, as expected. Most recently published volumes ofongoing comics increased sales of former volumes by 29.1%. TV animation series
boosted comic sales by 87.8%, and electronic promotion increased sales by 51.7%. Theseestimated figures are plausible from the view of Japanese comic business according to
my interview survey to the four publishers, which supports the validity of thisestimation.
The coefficient of reach sites is negative in column (1), indicating that piracy
reduced legal sales in the research period. However, when we divide the comics into
ongoing comics (column (2)) and completed comics (column (3)), the coefficients show
opposite signs, though one of them is insignificant. As for ongoing comics, a 1% increaseof reach sites reduced sales by 0.102%, whereas the effect on completed comics was not
significant and slightly positive. In other words, the displacement effect was dominant
in the case of ongoing comics, though some kind of advertisement effect worked in thecase of completed comics and cancelled the displacement effect.
Table 4 Basic case: ongoing comics vs completed comics(1)
All comics(2)
Ongoing
comicsLog(sale)(3)CompletedcomicsLog(sale)-0.0891***-0.102***(0.0287)(0.0305)time-0.0332***-0.0178***(0.00314)(0.00566)
log(trend)-1.242***-1.294***(0.0637)(0.0624)
newpub1
0.0286-0.0185(0.0339)(0.0369)
newpub2
0.291***
0.255***(0.0263)(0.0307)anime
0.878***
0.879***(0.0673)(0.0705)
e_promotion
0.517***
0.483***(0.0323)(0.0364)
Constant
4.976***
5.856***(0.0625)(0.0729)
Monthly dummies
includedincluded
Observations
24,562
15,754R-squared
0.205
0.232
Number of group
3,372
2,256
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.132(0.104)-0.0453***(0.00383)

VARIABLES

Log(sale)log(site+1)

16

1.059***(0.224)
0.638***(0.0753)
3.152***(0.211)included
7,356
0.115
932

The reason that advertisement effect appears for completed comics is supposed tobe that readers do not remember the completed comics because they ended several
years ago, and publishers do not advertise them. Piracy "reminds" consumers of theexistence of the completed comics of years ago, and handfuls of downloaders of pirated
files find them interesting. A small percentage of them, or those informed by word-ofmouth, purchase the comic. In contrast, ongoing comics are well-known because ofserial publication in weekly comic magazines and publishers' advertisements in thebook store. Therefore, the "remind effect" of piracy has relatively little effect on ongoingcomics.
To test the validity of this “remind effect” hypothesis, we divided the data into thefirst and middle volumes and recent two volumes. As for ongoing comics, publishers
and book stores advertise recent volumes in-store more than first and middle volumes.
Thus, the benefit of remind effect is minimal in the case of recent volumes of ongoingcomics since they are already advertised very well. Relatively speaking, then, therecent volumes suffer more from piracy than the first and middle volumes because
recent volumes obtain less benefit from the remind effect. Regarding completed comics,
people who notice the comic by remind effect and have interest in it will start to buy
the comics from the first volume, not from the most recent volume, and some readers
may stop reading before reaching the final volume.2 Therefore, the remind effect works
better for the first and middle volumes than recent volumes in the case of completed
comics. In summary, if the remind effect is working, recent volumes suffer from piracy
more for ongoing comics, and first and middle volumes benefit more from piracy whenit comes to completed comics.
To investigate this, we ran regressions of columns (2) and (3) of Table 4, dividing
the data into recent two volumes and first and middle volumes. Table 5 shows theresults. In the case of ongoing comics, the elasticity of the recent two volumes (-0.0967)
was two times higher than the first and middle volumes (-0.0397), showing that recent
volumes suffer more from piracy than the first and middle volumes. Regarding thecompleted comics, the coefficient of the first and middle volumes was significant andvery high (0.522). These results are consistent with the expected pattern discussed in
the previous paragraph, which supports the remind effect hypothesis.

It is common fact in the book market that when the book consists of two volumes volume 1 sells
more than volume 2. The reason is that there are some readers who buy volume 1 and quit buying
volume 2, whereas there is no reader who would buy volume 2 only.

2

17

Table 5 Comparison of recent two volumes and first & middle volumes

VARIABLES(1)
Ongoing
Recent twovolumesLog(sale)(2)
OngoingFirst & middle
volumesLog(sale)(3)Completed
Recent twovolumesLog(sale)(4)Completed
First & middle
volumesLog(sale)-0.0967**(0.0375)-0.0397(0.0246)-0.0684***(0.00466)-0.0401(0.110)-0.0529***(0.00592)

0.522***(0.115)-0.0377***(0.00481)

log(site+1)
time
log(trend)-1.578***(0.0718)

newpub1

0.168***(0.0360)
0.357***
0.386***(0.0630)(0.0274)
0.643***
0.981***
1.167***(0.155)(0.0655)(0.317)0.354***
0.531***
0.609***(0.0660)(0.0428)(0.107)7.836***
4.670***
3.678***(0.119)(0.0589)(0.221)includedincluded
6,478
9,276
3,663
0.269
0.214
0.123
1,088
1,168
467
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

newpub2
anime
e_promotion
Constant
Monthly dummies
Observations
R-squared
Number of group0.953***(0.314)0.664***(0.106)2.179***(0.247)
3,693
0.122
465

How far into the past does remind effect extend? To answer to this question, we
divided the completed comics by ending year and applied the same panel regression.
Figure 2 shows the coefficients of log(site+1) depending on the ending year of comics,
which is shown along the horizontal line. Regarding periods before 2010, several years
were grouped into one category in order to keep the sample size at a similar level(sample size is in parentheses). Figure 2 shows that the elasticities of reach sites werepositive for comics that ended in 2010-2013, thus suggesting that the remind effect was
effective for this period.
Interestingly, elasticities were almost zero in the near past (in 2014) and long ago(during 2005-2009). The fact that remind effect was ineffective for 2014 can be
explained as follows: consumers remembered the comic well because it had only beenreleased one year prior. Note that this paper’s research period is 2015-2016. If
18

consumers remember the comic well, there is no need for pirated files to stimulate
consumers' memories to encourage them to purchase the comic. The reason that
remind effect did not work before 2009 is uncertain. One of the possible explanations isthat consumers lost interest in the comics of that time period because the style orquality of comics had changed.
Figure 2
Effect of Reach Sites on Comic Sales(Coefficients of "log(site))"Completed comic only, by ending year, unit=elasticity
1.12

1.2
1.0
0.8

0.71

0.6
0.42
0.4
0.2
0.0
-0.08

-0.2
-0.20
-0.4
2014（1229)

2013(784)

2012(536)

2010-2011(536) 2005-2009(608)ending year

The displacement effect of ongoing comics may change over time. To demonstrate
this, we broke down the coefficient of log(siteit+1) of the regression (1) in Table 3 byreplacing the variable with cross term with dummy variables, Dj*log(siteit+1), where Dj
is equal to one if it is j months after the publication month. In other words, we replaced
b*log(siteit+1) with bj* Dj*log(siteit+1). Coefficients of this cross-term, bj , represent thetiming and strength of the displacement effect.
Figure 3 shows the result. The vertical line represents elasticity, coefficient of
Dj*log(site+1), and the horizontal line is j, months after the publication. This graphshows that the displacement effect worked mainly during the initial five months. Notethat this graph shows relative strength, assuming that elasticity was zero when t=0,
just published month; hence, the positive elasticities at t=9 and 10 do not necessarilymean displacement effect was reversed. We can safely say that the displacement effectwas concentrated during the first several months and declined rapidly after the initialperiods. This rapid decline of displacement effect of newly published volumes is also
19the reason why the remind effect becomes dominant in the case of completed comics.
Figure 3

Displacement effect in the time line
Recent volumes of ongoing comics only, unit=elasticity
0.3

0.21

0.2

0.17

0.2
0.1
0.03

0.1
0.0
-0.1
-0.1

-0.02

-0.03

6

7

-0.05
-0.10

-0.2

-0.13

-0.2
-0.3
1

-0.23
2

-0.21
3

4

5

8

9

10

month after the publication

Since studies on the effects of piracy in the book industry are seldom seen, we willshow a typical example for illustrative purposes. Figure 4 shows five cases. On thehorizontal axis is month, and vertical lines are sales for the left axis and number ofreach sites for the right axis. Figure 4(a) shows the case of newly published volumes
when there was no reach site with links to this comic. Without reach sites, sales
decreased continuously after publication. A slight increase in December was caused byanother new publication of this comic.
Figure 4(b) and (c) are the cases where reach sites listed links one month after the
publication. The volumes were published in September, but there were no links topirated files on the reach site at the month of publication. One month later, ten or
eleven reach sites listed the link on the sites, and then the sales declined sharply,
deviating from the trend. This deviation — that is, the sharp downshift of sales in
October (and November) — is interpreted to be displacement effect.

20

Figure 4(a) no piracy case: a comic for girls, second recent volume
Sales when there is no reach sites
Sales
100000In Decmber
New volume
is published

10000

# of
sites

80
70
60
50

1000

40
30

100

20
0

0

0

0

0

0

Aug

Sep

Oct

Nov

Dec

Jan

10

10

0
# of sites

sales(b) Displacement effect case1
a comic for girls, most recent volume
volumeSales

Sales and reach sites

100,000

displacementd effectFeb(c) Displacement effect case2
a comic for boys, most recent
Sales and number of reach sitesales

# of sites# of sites
50

1000

30

45

412

25

10,000

40
35

20

30

1,000
100

15
100

10

25

65
48

11
9

10

8

12

11

20

37
11

11

15
10

10

5

5

0

0
Sep

Oct

Nov

Dec

# of sites

Sep

Jan

Sales and reach sites

360
Remind effect
310

50

900

45

800

40

700

35

600

30

500

210

25

400

160

20

110

11

15
10

60
0

0

0

1

2

521
0

10
April May June July Aug Sep Oct Nov Dec Jan

Dec# of sites

sales

sales

# of sites

In April, second
recent vol. published30
In October, mostrecent vol. published

25
20
15

300

7

7

10

200
5
100

0

0

0

0

0

0

0

0
April May Jun July Aug Sep Oct Nov Dec# of sites# of sites

Jan

Sales and number of sites
Sales

260

11

Nov

(e) Remind effect case 2
a comic for men, middle volume# of sites

new volume
published in Aug

0

Oct

sales

(d) Remind effect case 1
a comic for men, the first volume
Sales

0

10

0

1

410

75

sales

Jan

Figure 4(d) shows the case when pirated files appeared on the reach site long after
publication. This comic started in 2012, and this graph represents the sales of the first
volume. Since three years passed after the publication, sales were low and gradually
decreasing (Note that the left axis is scaled not by log term but number term). A
sudden rise of sales in August was caused by the publication of a new volume. The
number of reach sites that had the link to this volume was zero until September andbegan to increase slightly in October and November. The number of reach sites jumped
to 11 in December, which is interpreted to have raised the sales in January, deviatingfrom the trend. This deviation from the trend is supposed to be the result of remind
effect. Figure4(d) is a case of a middle volume, the sales of which jumped after piracy
files appeared in December. We can interpret this jump as remind effect: consumers
read this comic via piracy files and found it interesting, and some of them or others
who heard about it through word-of-mouth purchased it.

VII DID regression
Fixed effect cannot cope with endogeneity if the comic's popularity changes during
the research periods and site administrators notice it quickly. If a comic suddenly
becomes popular in the middle of the research period, both the sales and the number ofreach sites (or links to pirated files) will increase simultaneously, which make thenumber of sites endogenous. Difference-in-difference analysis can cope with thisendogeneity problem because it does not use number of sites as an explanatory
variable.
We applied the DID model using the massive deletion project, which started in the
middle of research periods as a natural experiment to measure the net effect of deleting
pirated files on comic sales. A total of 551 comics were targeted by this deletion, andthese made up the treatment group. For control data, we chose 289 comics that were
not targets of the massive deletion project.
For difference-in-difference regression, treatment data and control data should havethe same characteristics except for the exogenous shock. Figure 5 shows the genre
distribution of the target comics (treatment) and non-target comics (control). Japanesecomics are ordinarily categorized into four genres: for boys, for men, for girls, and for
women. Light novel refers to comic-like novels in Japan. As Figure 5 shows, thedistribution pattern was similar between the treatment and control groups. Starting
year and ending year of the comic were also similar, as shown in the attached sheet in22

Figure 5. However, sales level was somewhat different; the table shows that total
annual sales of the first volume were 5,791 books for the treatment group and 3,220
books for the control group. This is because publishers chose big titles for this massive
deletion project. This bias was considered later in the regression analysis.
Figure 5

Genre distribution of treatment and control
40%

36%35%

35%
30%
30%

26%

25%

25%

21%
20%

starting year
ending year

15%
9% 8%

10%

total sales of thefirst volume

8%

5%

comics of comics oftreatment control(3
(579)
09)
2007.6
2007.82014.1

2013.3

5791

3220

2%

0%

for boysfor menfor girl

comics of treatment(579)

for women light novel

comics of control(309)

Unfortunately, the actual effect of this massive deletion project was limited sincedeleted files are quickly restored to cyber locker sites, and reach sites rewrite the links.
It is often the case that policy intervention against piracy does not affect the level ofpiracy. (Ushiyama, 2009; Bhattacharjee, Gopal, Lertwachara, & Marsden, 2006). Thus,
before estimating the effect of deletion, we had to examine the actual effect of thismassive deletion project. According to difference-in-difference approach,
Siteit = ai+ b0*DafterJulyt + b*Deletioni*DafterJulyt +c*Zit + eit(3)

Dependent variable Siteit represents the number of reach sites of comic i at month t.
DafterJulyt is equal to one if month t is after July, and Deletioni is equal to one if comici is a target of the massive deletion project. The cross-term coefficient of DafterJulytand Deletioni is an effect of the massive deletion project. Control variables Zit are
general time trends (time) and nonlinear trends ln(trend) for newly published volumes.
23

Since some cyber locker sites set expiration dates to the files on their server, pirated
files tend to disappear gradually even without the deletion request from copyrightholders. The time trend variable was introduced to represent this general tendency.
Regarding newly published volumes, the number of sites is usually initially zero andincreases nonlinearly. Ln(trend) represents this nonlinear trend.
Table 6 shows the result. As column (2) shows, the cross-term coefficient of completed
comics, -0.619, was small but significant. Therefore, we can say the massive deletionproject reduced the number of reach sites successfully for completed comics. Regarding
ongoing comics, however, as column (1) shows, the cross-term coefficient was not
significant. Ongoing comics are more popular than completed comics because theirepisodes are released weekly in serial comic magazines and often advertised bypublishers. Thus, it is natural that pirated files of ongoing comics would be restored topiracy sites more quickly than already completed comics. Quick and frequent reuploading offset the effect of the massive deletion project. Since the deletion of files is
uncertain regarding ongoing comics, we focus on the case of completed comics
hereafter.

Table 6 Effect of Deletion Project

VARIABLES
Djuly
Deletion*Djuly
time
log(trend)
Constant(1)
Ongoing
comicsSite(2)CompletedcomicsSite

0.0928(0.133)-0.141(0.176)-0.0713***(0.0179)
3.691***(0.249)6.109***(0.170)0.466***(0.107)-0.619***(0.139)-0.0166(0.0151)6.924***(0.105)Observations
9,369
4,480
R-squared
0.133
0.009
Number of group
1,345
560
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

24

Table 7 shows the result of difference-in-difference regression. The first two columns
represent the basic case. Column (1) is the case of ongoing comics, and column (2) is thecase of completed comics. Regarding ongoing comics, the cross-term coefficient ofDafterJuly and Deletion was positive (0.703) but not significant. This insignificancecorresponds to the insignificance of the deletion effect seen in Table 6. The positive sign
suggests that sales might increase with deletion, which is consistent with the result ofpanel regression seen in section VI.
As for completed comics, the coefficient is significant and -0.830, meaning that themassive deletion project reduced the sale of completed comics by 8.3%. This result
implies that pirated files increase the sales of completed comics, which is also
consistent with the result of panel regression, confirming the validity of the "remind
effect".

25

Table 7 Difference-in-Difference model(1)
Ongoing
comicsBasic case(2)
CompletedcomicsBasic case

VARIABLES

Log(sale)Log(sale)

DafterJuly

0.0837*(0.0439)0.0703(0.0536)-0.0584***(0.00499)-0.722***(0.0569)-0.00776(0.0318)
0.112***(0.0312)0.976***(0.0751)0.508***(0.0362)5.390***(0.0425)

0.0480(0.0357)-0.0830**(0.0398)-0.0600***(0.00373)

Deletion*DafterJuly
time
log(trend)
newpub1
newpub2
anime
e_promotion
Constant
Monthly dummies
Observations
R-squared
Number of group21,074
0.166
2,279

(3)
Ongoing
comicsWith monthly
dummiesLog(sale)(4)CompletedcomicsWith monthly
dummiesLog(sale)

0.0672-0.0800**(0.0537)(0.0398)-0.0654***-0.0653***(0.00630)(0.00515)-0.724***(0.0565)-0.0294(0.0325)
0.121***(0.0318)0.862***
0.976***
0.859***(0.187)(0.0750)(0.188)0.689***
0.408***
0.656***(0.0747)(0.0370)(0.0762)3.446***
5.434***
3.488***(0.0203)(0.0610)(0.0458)
includedincluded
10,09321,074
10,093
0.141
0.175
0.146
932
2,279
932
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(5)
Ongoing
comics100<sale<5000
Log(sale)(6)Completedcomics100<sale<5000
Log(sale)-0.0547(0.0456)
0.154***(0.0559)-0.0609***(0.00583)-0.410***(0.0720)0.0412(0.0384)
0.102***(0.0371)0.864***(0.106)
0.671***(0.0412)4.689***(0.0499)

0.0562(0.0405)-0.111**(0.0469)-0.0684***(0.00544)

12,787
0.158
1,298

5,493
0.191
517

0.604***(0.179)0.875***(0.104)4.301***(0.0278)

Columns (3)–(6) in Table 7 show a verification of the robustness of this result.
Monthly dummies are included in columns (3) and (4) to remove temporary shocks.
Columns (5) and (6) limit the data to comics whose sales (measured by annual total
sales of the first volume) were between 100 and 5000 in order to consider the differenceof total sales level between the treatment and control groups shown in Figure 5. In
both cases, cross-term coefficients were positive for ongoing comics and negative forcompleted comics. The results of the basic case were maintained
Regarding completed comics, we checked robustness further. We focused oncompleted comics because the number of sites were significantly decreased by themassive deletion project, and the fact that piracy increases legal sales is a controversial
topic in copyright issues. The robustness of regression (2) is verified in Table 6, a basic
case of completed comics.
26

Although a placebo test is as common as a robustness check for difference-indifference approach, we could not apply it here because the sample size in the timeseries was too small (12 months). Instead, we divided the sample into subgroups andexamined the correlation between deletion and sales. If correlation among subgroups
was consistent, this suggests that the result was not caused by specific small data, butreflects an overall tendency. This subgrouping check is the approach adopted byDanaher and Smith (2014) to investigate the effects of the shutdown of major cyberlocker site, MegaUpload, on DVD sales. Since the penetration of MegaUpload was
different among countries, they correlated the countries' sales change after the
shutdown with the penetration ratio of MegaUpload and found a positive correlation.
In other words, they divided the worldwide consumers into country subgroups andchecked the correlation between the degree of intervention (penetration of
MegaUpload) and outcome (DVD sales) in the difference-in-difference model. We
applied this approach as well.
Before making subgroups, we applied an ordinary stepwise Chow test to see
whether July was the proper timing for applying DID regression. The null hypothesis
was that coefficients of both DafterJuly and DafterJuly*Deletion were zero in the
regression (2) of Table 7. Figure 6(a) shows the F-value of Chow test changing the
hypothetical starting point of massive deletion from May to December. Clearly, July is
the most probable point at which structural change occurred, which suggests the July
was the proper point to apply difference-in-difference regression.
Firstly, we divided the completed comics by ending year into five groups and ran
DID regressions of sites and sales. We then checked correlation of cross-term
coefficients. Let bsitey be coefficients of cross term, DafterJuly*Deletion, of theregression of the reach site (model (3)) and let blog(sale)y be cross-term coefficients of theregression of the sales (model(2)) when the data is limited to comics ending in year y.
Both terms measure changes caused by the massive deletion project, and bsitey
represents the change in number of comics sites ending in year y, and blog(sale)yrepresents the percentage change of sales of comics ending in year y. If piracy reduces
legal sales, bsitey and blog(sale)y have a negative correlation in terms of y because, whenmore sites are reduced for the comic group y, then more sales increase for the samecomic group y. To the contrary, if piracy increases legal sales, they correlate positively.
Figure 6(b) shows the result. The horizontal line is bsitey, change in number of sites, andthe vertical line is blog(sale)y, change in sales. In our findings, we observed weak positive
correlation, which means that comics deleted more are sold less in terms of ending year
grouping. This result suggests that piracy increases legal sales.
27

Figure 6 Robustness check of remind effect of completed comics(a)(b)
change of number of site VS sales, by ending year

F-values of stepwise Chow test, by DID timing
4.50
4.00
3.50
3.00

change of sales after the massive deletion(%)

0.1

5.00

-0.9

2.50
2.00
1.50
1.00
0.50
0.00
5

6

7

8

9

10

11

12

2012

2010-2011
-0.8

-0.7

-0.6

-0.3

-0.2

-0.1

0

-0.1

-0.15

(d)
change of number of site VS sales, by genre

Jan
0.1
0.05
0

Dec

Oct
-0.6

-0.4

-0.2

Aug
Nov

0

-1.4

-0.05
-0.1

Sep
July

change of sales after the massive deletion(%)

0.15

change of sales after the massive deletion
term=elasticity

0-0.4

change of number of sites after the massive deletion
unit=# of sites

change of number of site VS sales, by month

-0.8

2014

2013

(c)

-1

-0.5

-0.05

month

-1.2

0.05

2005-2009

-0.15

0.2
for girls
0.15
0.1

-1.2

-1

-0.8

-0.6

-0.4

0.05
light novel
for men
0
-0.2
0
-0.05
-0.1

for women
for boys

-0.15
-0.2

-0.2

change of number of sites after the massive deletion
unit=# of sites

change of number of sites after the massive deletion
unit=# of sites

Secondly, we broke down the cross-term coefficient into monthly terms by replacing
DafterJulyt with Djulyt, Daugt, Dsept, …, Ddect, which are equal to one if t is equal tothe current month, as in Figure 3. Since the performance of the massive deletionproject may change month to month, the degree of reduction of reach sites also maychange over time. If the sales of comics positively correlate with the change in site
reduction by monthly terms, this is supporting evidence of the result. Thus, we
replaced DafterJulyt in the site regression (model (3)) and sales regression (model(2))
with a set of Djulyt, Daugt, Dsept,…Ddect , and plotted the cross-term coefficients, bsitej
28

0.2

and blog(sale)j . The results are shown Figure 6(c), indicating positive correlation, whichmeans that sales decreased more in the month when reach sites decreased more. This
result again suggests piracy increases the legal sale of completed comics.
Thirdly, we let the coefficients change depending on comic genres. As previously
mentioned, the Japanese comic industry has five genres: for boys, for men, for girls, forwomen, and light novels. Genre dummies Dg were introduced to correspond to thesegenres, and cross-terms of models (2) and (3) were replaced with Σblog(sale)gDg*DafterJulyt*Deletioni, and Σbsiteg*Dg*DafterJulyt*Deletioni, respectively.
Coefficients b log(sale)g represent sales change of comics in genre g, and bsiteg represent
site change of comics in genre g. Correlation of these coefficients is shown in Figure6(d), which indicates positive correlation again. Therefore, if comics of a genre aredeleted more than other genres, the sales of comics of the genre decrease more thanother genres. This result suggests that piracy increases legal sales with regard tocompleted comics.
In Figure 6(b)(c)(d), we try to break down cross-term coefficients of difference-indifference regression by subgrouping data with the purpose of examining theconsistency of degree of policy intervention (deletion) and its outcome (sales). Allgraphs show that correlation is positive; thus, they are consistent with the results inTables 2 and Table 5. No matter which subgrouping is used, either by ending year, by
timing, or by genre, more deletion leads to less sales, which means piracy stimulates
sales as far as completed comics are concerned.

VIII Summary and Discussion
In this paper, we examined the effect of piracy on the comic book market in Japan
using direct measurement of product level piracy ratio and a massive deletion project
as a natural experiment. We found the piracy effect was heterogeneous depending onwhether the comic series was ongoing or completed. Piracy decreased the legitimate
sales of ongoing comics but stimulated legitimate sales of completed comics:
displacement effect was dominant for ongoing content, and advertisement effect was
dominant for completed content. We can interpret this result as an indication that thepiracy reminds consumers of past comics that are no longer promoted by publishers.
An interesting question for further research is whether the total effect of piracy isnegative or positive. Unfortunately, we were unable to calculate this because the sales
share of ongoing comics and completed comics is unknown. The loss of displacement
effect is far larger than the gain of remind effect per individual book base, since29

ongoing comics usually sell over ten thousand copies in the newly published month,while most completed comics series sell only several hundred in a month. However,volumes of completed comics far outnumber newly published volumes of ongoing series.
Which effect is dominant depends on the total sales share of ongoing and completed
comics, and this figure is not available.
An important point we should stress here is the policy implication of theheterogeneity of piracy effect. If the effect of piracy is heterogeneous, it is not the bestsolution to shut down the piracy sites but to delete harmful piracy files selectively if
possible. In this case, deleting piracy files of ongoing comics only is the first best
strategy for publishers regardless of whether the total effect is positive or negative,
because the availability of piracy files of completed comics is beneficial to bothpublishers and consumers.
Heterogeneity is not limited to the book industry. In 2007, Blackburn showed thatfile sharing decreased CD sales of big artists, whereas it increased CD sales of
unknown minor artists. Bhattacharjee et al. (2007) reported that file sharing reduced
survival time in the Billboard CD album ranking for low-ranked albums but did not
hurt top-ranked albums. Mixed results in the literature of piracy studies so far may becaused by the heterogeneity of products. Product level research that takes thisheterogeneity into consideration may shed some light on the effects of piracy. This
paper's contribution is the addition of new dimension to the heterogeneity — present
content and past content.

30

References
eferences
Adermon,Adrian, Che-Yuan Liang, 2014,"Piracy and music sales: The effects of an antipiracy law", Journal of Economic Behavior & Organization 105 pp.90–106
Arnold, Michael, Eric Darmon, Sylvain Dejean and Thierry Penard 2014 "GraduatedResponse Policy and the Behavior of Digital Pirates: Evidence from the French ThreeStrike (Hadopi) Law" Working paper NO. 2014-07m, Alfred Lerner College of Business& Economics, University of Delaware
Bhattacharjee, Sudip, Ram D. Gopal, Kaveepan Lertwachara, and James R. Marsden,
2006 "Impact of Legal Threats on Online Music Sharing Activity: An Analysis of Music
Industry Legal Actions," The Journal of Law and Economics 49, no. 1 (April 2006): 91114.
Bhattacharjee,Sudip,Ram Gopal, Kaveepan Lertwachara, James Marsden, Rahul
Telang, 2007, "The Effect of Digital Sharing Technologies on Music Markets: A Survival
Analysis of Albums on Ranking Charts", Management Science 53(9), pp1359-1374
Blackburn, David, 2007, "The Heterogenous Effects of Copying: The Case of RecordedMusic", Working Paper, Harvard University, Cambridge.
Danaher, Brett, Michael D. Smith 2014 "Gone in 60 Seconds: The Impact Movie Sales,"International Journal of Industrial Organization 33 (2014) 1–8
Danaher, Brett, Michael Smith, Rahul Telang, and Siwen Chen 2014 "The Effect ofGraduated Response Anti-piracy Laws on Music Sales: Evidence from an Event Studyin France," Journal of Industrial Economics 62(3) 541-553
Danaher, Brett, Michael Smith, Rahul Telang, 2013, "Piracy and Copyright
Enforcement Mechanisms" NBER Working paper 19150
Hardy, Wojciech, Michal Krawczyk and Joanna Tyrowicz, 2014, "Internet Piracy andbook sales: a field experiment," Working papers NO.23, Faculty of Economic Science,
University of Warsaw

31

Hilton, J., Wiley, D., 2011. "Free e-Books and print sales," Journal of Electronic
Publishing 14 (1).
http://hdl.handle.net/2027/spo.3336451.0014.109
Hu, Y. J., Smith, M. D., 2013. The impact of ebook distribution on print sales: Analysis
of a natural experiment. SSRN Scholarly Paper ID 1966115, Social Science Research
Network.
Liebowitz, Stan J., 2006, "File Sharing: Creative Destruction or Just Plain Destruction?"
Journal of Law and Economics 49(1) pp. 1-28
Liebowitz, S. (2008), ‘‘Testing File Sharing’s Impact on Music Album Sales in Cities’’,Management Science 54, 852–9.
Martikainen, E., Jan. 2011. Does file-sharing reduce DVD sales? SSRN Scholarly Paper
ID 1742443, Social Science Research Network.
Oberholzer-Gee, F. and Strumpf, K., 2007, ‘The Effect of File Sharing on Record Sales.
An Empirical Analysis,’ Journal of Political Economy, 115(1), pp. 1–42.
Oberholzer-Gee, Felix; Strumpf, Koleman.,2010,"File Sharing and Copyright",
Innovation Policy and the Economy 10, pp. 19-55.
O’Leary, B., May 2009. Impact of P2P and Free Distribution on Book Sales. O’Reilly
Media, Inc.
Peitz, Martin and Patrick Waelbroeck,2008, "Piracy of Digital Products: A Critical
Review of the Theoretical Literature," Recent Trends in the Economics of Copyright,
2008, pp. 125-52, The International Library of Critical Writings in Economics
series ,Edward Elgar, Also available at SSRN: https://ssrn.com/abstract=889128 or
http://dx.doi.org/10.2139/ssrn.889128
Smith,M.D.,R.Telang.2009. "Competing with free:The impact of movie broadcasts on
DVD sales and Internet piracy." Management Information Systems Quarterly 33(2)321–
338.

32

Takeyama, Lisa N..1994," The Welfare Implications of Unauthorized Reproduction ofIntellectual Property in the Presence of Demand Network Externalities." Journal ofIndustrial Economics 42(2), pp. 155-66.
Tanaka, Tatsou. 2004. “Does File‐Sharing Reduce CD Sales? A Case of Japan.”Paper prepared for Conference on IT Innovation, Hitotsubashi University,
Tokyo.
Ushiyama, Atsushi, 2009, "Crime on the Incidence of Crime: A Comparison with
Robbery," Japanese Economy, 36 (2), pp. 78-102
Zentner, A. (2005), ‘‘File Sharing and International Sales of Copyrighted
Music: An Empirical Analysis with a Panel of Countries’’, B. E. Journals
in Economic Analysis and Policy: Topics in Economic Analysis and Policy
5(1), 1–15.

33