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TITLE: “How does Cannibalization Affect the Timing of New Product Introductions? Evidence
from US Video Games”
AUTHOR: Hiroshi Ohashi
Department of Economics, University of Tokyo,
ABSTRACT:
This paper investigates the release date scheduling of US video games in the period from 1994 to
2001. A particular focus is given on how the ownership structure affects the release timing of video
games. A typical feature of the video game diffusion pattern makes the release timing decision
crucial to the success of a new game. The evidence suggests that games under joint ownership are
released in more space than those owned by different publishers. Furthermore among the joint
publishers, platform providers achieve more efficient outcome than non-platform providers. The
paper associates the finding with industry practice evolved in the modern video game market.
Keywords:
Video games; software; release timing; Tobit; cannibalization
JEL: L13; L86; M31
� This paper is NOT intended for the Young Economist Award Competition.
How does Cannibalization Affect the Timing of New Product
Introductions? Evidence from US Video Games
Hiroshi Ohashi ∗
February 2005
Abstract
This paper investigates the release date scheduling of US video games in the period from 1994
to 2001. A particular focus is given on how the ownership structure affects the release timing
of video games. A typical feature of the video game diffusion pattern makes the release timing
decision crucial to the success of a new game. The evidence suggests that games under joint
ownership are released in more space than those owned by different publishers. Furthermore
among the joint publishers, platform providers launch their games more efficiently in timing than
non-platform providers. The paper associates these findings with industry practice evolved in
the modern video game market.
Keywords: Video games; software; release timing; Tobit; cannibalization; diffusion pattern
JEL: L13; L86; M31
1 Introduction
Success or failure of new products often hinges on the firm’s decision regarding the timing of the introduction
of new products (Urban and Hauser, 1993). Poor execution of product launch is often cited as a reason
for commercial failure of new products. The release timing of new products is a complex decision, being
influenced by various factors, including the effect of new product introduction on a firm’s existing product
line as well as competitor’s, consumer perception of the new product, and the size of the product market.
Although the release timing of new products is a firm’s important marketing strategy, there is little empirical
work that investigates this issue in the economics literature.
In this paper, we conduct such an empirical study using data of U.S. video game software. The release
timing of video games is one of the firm’s crucial strategy in the industry, because of the fast depreciation in
the popularity of games. Indeed, as we will discuss in Section 2, a time-path of unit sales of video game title
looks like ‘L’-shape: Much of the sales is made in the early stage of the product lifecycle. The front-loading
adoption pattern of video game sales is a common feature of the entertainment industry, such as movies
∗This paper is an outgrowth of the joint project with Matt Clements. I thank Makoto Abe, Matt Clements, Toshihiro
Matsumura, Dan Sasaki and Jun-ichiro Shintaku for helpful comments. I also thank Hironori Ishii and Naoto Tominaga in
Taito, Co. for providing information on the US game market.
1
(Krider and Weinberg, 1998; Einav, 2003) and music albums (Moe and Fadher, 1998), and it makes a stark
contrast with the conventional ‘S’-shaped pattern for other consumer electronics products (see Bass, 1969,
for discussion). This typical feature of the video game sales pattern, in theory, may make the release timing
decision particularly crucial to the success of the new product, and hence to increase in firm’s revenue.
The release timing of new products has drawn theoretical interest in the economics and marketing lit-
eratures. Theoretical work studies the impact of the competitive market environment on the timing of
new product introductions. Wilson and Norton (1989) and Moorthy and Png (1992) analyze a monopolist
problem in the timing decision of line extension. They show that a multi-product firm has an incentive to
launch their new products of the same quality level farther apart to avoid negative externalities (i.e., canni-
balization) on each other’s revenues. Desai (2001) adds a caveat to the above work in that a multi-product
firm would launch their products close each other if each of the products is served to a different consumer
segment. These theoretical implications have been largely escaped from the interest of empirical researchers,1 and to my knowledge, Corts (2001) is the only exception. In his application of the US film industry in
1995 and 1996, Corts (2001) asks how vertical market structure affects the scheduling of a pair of films, and
finds that jointly produced and distributed films internalize the cannibalization externalities. Merely joint
production or joint distribution is not enough to internalize cannibalization in his study, and Corts (2001)
attributes this observation to the consequence of incomplete contracting between producers and distributors.
In this paper, we focus on the US video game market in the period from 1994 to 2001, and apply the
empirical framework in Corts (2001) to the video game market. This paper contains three major differences
from Corts (2001), two of which are due to the nature of the market under study, and the other due to the
nature of our data set. The first difference is the paper’s finding that only joint distribution internalizes
competitive externalities on revenues of their games. This is perhaps due to the fact that, in contrast
to the U.S. movie industry, sell-out contract is predominant in the video game industry. Hence game
distributors (we also call them publishers) own the residual claim of the profit accrued from software sales.
The second difference is that, among the multiple-game publishers, platform providers do a better job in
spacing out release dates than non-platform providers. This observation may reflect industry practice in
the game publication process, in which platform providers influence strong authority as to which games to
be published on the platform. The last difference is due to the nature of the data. Because of the short
product cycle and intense inter- and intra-generational rivalry, we observe multiple incompatible platforms
in the market. This cross-sectional dimension, which lacks in Corts (2001), provide sufficient data variation
for us to control for unobserved effects that vary over the lifecycle of games. Since the incentive of game
publishers to launch their games differs by the lifecycle stage of the platform, it is important to control for
such unobserved effects in the analysis of video games.
The organization of the paper is as follows. Section 2 describes important features of the U.S. video game
software market. Section 3 gives descriptive statistics from our data set and presents the empirical analysis.
Section 3 reports the results. Section 4 concludes.1Empirical work has been mostly concerned with measuring the effect on the release timing of aggregate demand fluctuations
(Radas and Shugan, 1998; Axarloglou, 2003), the effect of quality differences of two new products (Krider and Weinberg, 1998),
and the relationship between the timing of new product introduction and its market success (Lilien and Yoon, 1990).
2
2 The Market of US Video Game Software
In this section, we describe the US video game market in our study period from 1994 to 2001, with a special
emphasis on game software. We illustrate the role of game publishers in the industry, and highlight the
importance of the release timing decision in game publishing. This section comprises two subsections. We
first describe the market structure of the industry, and then discuss sales pattern of video games.
2.1 Market Structure
The purpose of this subsection is to describe the role of a publisher in the working of the video game industry.
A home video game system consists of platform (the video game console 2) and compatible software (game
titles). The first home video game system was introduced in the late 1970s by Atari, but it was not until
1986 that the US market started growing when Nintendo launched its 8 bit system, Nintendo Entertainment
System (NES). Viewing that it was a flood of poor-quality games that triggered the fall of Atari (sometimes
called the “Atari Shock”), Nintendo protected its game with a security chip that locked out unauthorized
game cartridges. The only way for publishers to make games for the NES was to obtain design specifications
to unlock the security chip, and Nintendo maintained final authority in deciding which games would be
manufactured and in what quantities. This Nintendo’s authorization method to ensure the quality of games
has been adopted by subsequent platform providers, and led the US market for home video games to an
enormous growth. In the period of our study, software sales almost tripled, from 39 million units in 1994 to
112 million in 2001. Total revenue for the industry ($9.4 billion) exceeded total box-office revenues in the
movie industry in 2001 ($8.4 billion).
Table 1 shows important aspects of market structure in the U.S. video game software market. Eight major
game platforms manufactured by three companies are listed in descending order of the total units sales in
our sample period from 1994 to 2001. The platforms in the table cover more than 99% of the U.S. game
software sales at that time. Games are produced on cartridges or discs (CD or DVD) for use with console.
Platform providers (like Sony) design and manufacture console and charge license fees to firms producing
software. Platform producers generally produce some of their own software, and many independent firms
produce software for one or more consoles.
The table presents two indicators of the annual market share. The first row in each platform (% units
sold) indicates the unit sales market share, and the second row (% variety) presents the share in terms of
the number of game titles sold by given year. An interesting observation is that the unit sales share tends to
be higher than the variety share in the early stage of the life cycle of a particular platform, and this ranking
reverses toward the later stage. This is consistent with the hypothesis that many game publishers waited to
provide for a video game system until blockbuster titles came along on the system. Under this hypothesis,
it is reasonable for a platform provider to develop and publish high-quality software by themselves to help
jump-start its console sales. To confirm this claim, we report shares of the units and titles sold by platform
providers in Table 1 (% platform units and % platform variety in the third and fourth rows respectively).
Indeed, except for Genesis, a platform provider made more sales per game title on average in the early stage of
its platform lifecycle. Taking advantage of the panel feature of our data, we control for this platform-lifecycle
2We use the two terms, console and platform, interchangeably in the rest of the paper.
3
effect by using the time and console fixed effects in the estimation discussed in Section 4.
Table 2 introduces several descriptive statistics on the software market. The first block of the table shows
ranking of software publishers and game titles. Software publishers provide finance for game development,
manage relations with platform providers, and perform packaging and marketing for game titles. Marketing of
game titles involve extensive advertising in the press, and promotion at the trade shows, such as the Consumer
Electronic Show (CES; held in every January during our study period) and the Electronic Entertainment
Expo (E3; held in every May). Those publishers that own sales representatives offer incentives directly to
retailers to obtain better shelf space, but have little control over retail prices. A publisher can be a platform
provider, a game developer (Electronic Arts and Acclaim, for example), or independent of these two. As
mentioned above, a platform provider has strict terms that give authority over the license agreement on
which the publishers have to sign to make games for the platform.3 The license agreement contains the
amount of a royalty fee that a third-party publisher pays to a platform provider for every unit of game title
sold. It is generally regarded that publishers own the residual claim of profit accrued from game sales.
Table 2 shows that about 18 % of the games were published by the platform providers. While half of the
games were marketed by the top ten publishers, the rest of them were handled by more than a hundred of
independent publishers. The game publishing industry hence looked fairly competitive. The revenue ranking
demonstrates that the top five publishers controlled more than 50 % of the industry revenues, and that only
a handful of publishers shared a majority of the revenue of $21.7 billion over the seven years.
This concentration of industry revenue implies significant economic scale in game publishing. This feature
is generated by the fact that the cost of developing a new game title has swollen considerably with the increase
in complexity and capacities of a game console. According to Coughlan (2001), the estimated average cost
climbed from $80,000 for a 8-bit game up to $6 million for a 128-bit. These development cost were recouped
by revenues from blockbuster games. The most popular game (Super Mario) made more than quarter billion
dollars (indicated in Table 2). The top five percent of the titles made more than 50% of the software industry
revenue in the period from 1994 to 2001. Surging software development cost has therefore consolidated the
ownership of blockbuster game titles into a few large publishers. The publishers generally hoped to market
each game title for as many platforms as possible, however, converting a game from one system to the
other would take additional development time and cost. Furthermore, contractual agreements with platform
providers sometimes required exclusivity to one game system, either for a limited time period, or sometimes
indefinitely. It is only 32 % of the game titles actually provided to other platforms, in contrast with the fact
that more than 80% of the titles were published independently.
2.2 Demand Structure
A typical adoption pattern of the video games highlights the importance of the release date scheduling in
game publishing. Figure 1 traces monthly unit sales of game titles after the market launch.4 The figure
classifies game titles in terms of the life period of games (in months). We make the distribution of life period
over the total number of games of 4015 titles, divide up the distribution at the 30th and 70th percentiles,3We detail this screening process in Section 4.4As discussed in Section 3.1, our data are of monthly frequency. Since the sales at the introduction month differ whether
the game was introduced earlier or later in the month, Figure 1 traces the sales from the following month of the introduction.
4
and take the average of monthly sales within each of the three regions. The figure includes games that are
truncated from the right, however, excluding the truncated data does not change our discussion below.
An observation worth noting is that unit sales rapidly decline with age. Figure 1 shows that approximately
38 (67) % of the sales were typically made in the first three (six) months after the game release. This is so
regardless of the fact that retail prices drop substantially in the course of games lifecycle. Table 3 shows the
average prices by console and by age of games. Retail price is controlled by retailers, normally beyond the
control of publishers. The annual rate of price drop is rapid in the first year of the game introduction at
38 percent, and declines over time to 17 percent six years afterwards. Figure 1 and Table 3 indicate that
price and unit sales both decline as a video game ages. In the marketing literature, this front-loading, or
‘L’-shaped, sales structure had been neglected, or sometimes regarded as failing to achieve the ‘S’-shaped
diffusion pattern. With the boom of the entertainment and IT-related industries, it is recognized that the
L-shaped diffusion pattern is a typical feature of successful products in these emerging industries (Furukawa,
Kato, and Yamada, 2002). The L-shaped diffusion pattern is primarily due to the fact that a video game
receives more attentions prior to the time of its market introduction. A publisher of the game runs advertising
on TV and magazines, and makes extensive promotion at trade shows, and provides incentives to retailers,
in order to seize consumers attention. The attractiveness of the game, however, fades away quickly after
several months into its introduction, being replaced by a flood of subsequent new game titles.
The diffusion curve in Figure 1 is roughly determined by two parameters, the intercept at age zero
(opening attraction) and the slope of the curve (decay). The opening attraction depends on publisher’s
intensity of marketing activities prior to the game introduction, and the decay depends on the degree of
information spillover (word of mouth, for example), competition with other games, and the level of retail
price. Better word of mouth, less competition, and lower price slows the decline rate of video game sales.5
When the video game launches, its sales performance depends more on the game itself, rather than on what
publishers do with advertising. Thus the opening attraction is the important measure to the product’s
future success. Holding the marketing activities and game characteristics constant, an important strategic
variable for a game publisher is the release-date timing. Games that are released close together would hurt
each other’s opening attraction, and thus their present and future revenues. For those games under the
same ownership, publishers should have a control in spreading the release dates in order to internalize such
negative externalities. If games are in the hands of different publishers, it would be difficult to avoid the
effect of these externalities. The empirical part of this paper investigates whether we observe such an effect
of the ownership structure on the release dates scheduling, and if it exists, how significant the effect is. We
now turn to describing the econometric framework to test this hypothesis.
3 Data and Estimation Method
In the previous section, we demonstrated that the release timing of games is an important strategic decision
variable for game publishers. We describe in this section the empirical framework to analyze how the
ownership structure influences the release date scheduling. We adopt the empirical framework proposed by5 Indeed, the adoption curve in Japan, where game prices are more stable, is steeper than that in Figure 1 (See Shintaku, et
al, 2003).
5
Corts (2001), and take a unit of observation as a pair of video games within a demand window we define
below. We examine how the spacing in the release dates of a pair of games is explained by a number of game
characteristics and control variables. The reminder of the section consists of two parts. In Section 3.1, we
briefly describe our data and variables used in the estimation. In Section 3.2, we explain how we exploit the
information on industry practice in game advertising so as to identify demand window.
3.1 Data
Our data on video games come from the NPD Group, a market research firm. The data contain retail
revenues and quantities sold for each game title existed in the period from 1994 to 2001, classified by
console, by publisher, and by genre. Table 2 presented the publisher information. The data identify 12
genres, and we describe this variable below in this section. The average retail price of a game title in Table 3
was calculated from the data of revenues and quantities. Clements and Ohashi (forthcoming) use the same
data set in the analysis of indirect network effects, but their variables are aggregated at the level of console.
This paper exploits the data disaggregated by individual game title, and analyze the effect of ownership
structure on the introduction timing of games. Since the feature of the data set is detailed in Clements and
Ohashi (forthcoming), this section only highlights the data aspects that are essential to the estimation.
We have monthly data for the period from January 1994 to December 2001. Console-level statistics were
presented in Table 1.6 We have to limit the scope of our analysis because of the use of this data frequency,
and we discuss the issue in the next subsection. Following the analytical framework of Corts (2001), we
make pairs of games launched during the demand window. We define the window in the next subsection. We
analyze how the spacing in the release dates of game pairs are explained by ownership structure, conditional
on control variables. The independent variable, GAPi,j,g, is thus the gap in the release dates (in months in
absolute value) between any given pair of games i and j, both of which are made for the same platform g.
The competitive pressure exists only between games compatible with the same platform. Since PS2 is made
compatible with PS, we assume that the PS2 games competed with the PS games. We are interested in the
effects of ownership structure in the analysis. We use two ownership dummy variables, each of which takes
either 0 or 1: SPDPi,j,g takes one when a pair of games, i and j for console g, are released by the same
non-platform publisher. When a game pair is launched by the same platform provider, we set SPDPi,j,g
equal one. The significance and magnitude of the coefficients of the two dummy variables are thus measured
in comparison with the effect on GAPi,j,g of games, each of which are released by different third-party
publishers.
Our data classify an individual game into twelve different areas of genre: action; adventure; arcade;
children’s entertainment; driving; family entertainment; fighting; role playing; shooter; simulations; sports;
and strategy. Some games are assigned to multiple genres. For example, Space Invaders, published for
N64, is classified in the genres of both arcade and shooters. The dummy variable, SGi,j,g, takes one if a
pair of games, i and j for console g, share at least one genre, and zero if no common genres exist between
them. Other characteristics being constant, games in the same genre are considered to be in rivalry. We
thus expect that the sign of the SG coefficient is positive, and games of the same genre are dispersed in the6We excluded the two latest game systems, Nintendo Game Cube and the Microsoft Xbox, both introduced in the late 2001.
6
release timing, compared with games, which are classified in different genres (We omit the subscript on the
variable hereafter unless it causes confusion).
The explanatory variables discussed above would not be likely to control for important heterogeneity
in the data. In particular, we found in Section 2 that the timing of games entry changes by each stage of
product cycle. Publishers appear more reluctant to make their games for the console whose lifecyle is at the
initial stage. As the platform attracts a certain size of console adopters, more publishers tend to launch their
games for the platform. Near the end of the cycle, when a platform is in decline, publishers gradually lose
their incentive to provide games for the old console. Taking advantage of the panel feature of our data set,
we control for the platform’s lifecycle effect by incorporating the platform and year fixed effects and their
interaction terms in the estimation.
3.2 Demand Window
In Section 2.1, we discussed that the CES in January and E3 in May are the most important exhibitions
for publishers to advertise their video games. Normally publishers display games at the CES for the release
in the following five months of the year, and at the E3 for the reminder of the year. We thus use this
information on industry advertising practice to divide the year into two distinctive windows.
We further refine our data to make them more appropriate for the analysis of the release-date scheduling.
Since this paper focuses on the supply side issue, namely the effect of ownership structure on the release
date competition, we would like to abstract the demand side effect from our analysis. As the monthly sales
statistics in Table 2 implies, the demand structure in November and December appear different from that
in the rest of the year: The game market booms in the holiday season from Thanksgiving Day through the
Christmas with the monthly sales more than doubling the sales of other month. Publishers are compelled
to adjust the release dates week by week during the end-of-the-year holiday season. On the other hand, the
monthly demand during the rest of the ten months appears stable with no visible spikes, and the publishers
focus more on their own game portfolio in the release date scheduling, with their decision horizon presumably
much longer than a week. 7 Thus we drop the data of November and December from our analysis, making
the data set more appropriate for our purpose. As a result, we create two demand windows in each year,
one from January to May, and the other from June to October. Approximately 3018 titles, 75 percent of the
total 4015 game titles, were launched during the months over the seven years.8 We pair games published
on the same platform in the period of the same demand window. In total, we have 35 windows (due to two
windows over seven years), and 283211 game pairs in the data set.
4 Empirical Results
This section estimates the effect of the game ownership structure on competition by focusing on the release
timing scheduling. We regress the difference in the release dates of two games onto the explanatory variables
introduced in the previous section. Summary statistics of the variables are in Table 4. Several observations7This point is also mentioned in the conversation with US sales representatives for Taito, Co, a game publisher.8Many game titles avoided the last two months of the year, partly because of steep advertising expenses incurred in the
period.
7
are worth noting. The independent variable, GAP , takes an integer between 0 (when the two games are
launched in the same month of the year) and 4 (the maximum in the demand window of five months).
Considering the nature of this variable, we use a Tobit model with two-sided truncation as the base estimation
method. Approximately eight percent of the total pairs are launched by the same publisher, and about half
of them are third-party non-platform publishers. Our identification in the effect of ownership structure comes
from this 8 percent of the sample against the rest of pairs published by different publishers, conditional on
the control variables. Looking at the sample means of the control variables, more than 80 percent of the
pairs are published in the year of 1994. Indeed about 36 percent of the total titles in the data are introduced
in 1994, and pairing them increases the number of observations exponentially for that year. 9 For platform
dummies, Genesis accounts for 40 percent of the pairs, followed by SNE (24%) and NES (21%). In addition
to the control variables listed in Table 4, we include the interaction terms of the year and platform dummies
in the estimation.
Estimation results are presented in Table 5. Result I shows three estimation results based on the pooled
sample. The ordinary least squared (OLS) estimates are presented under column (A) with heteroskedasticity-
robust standard errors. The Tobit estimates are under columns (B) and (C). We do not control for het-
eroskedasticity in the Tobit model, because no standard correction method is readily available in the litera-
ture. Both (A) and (B) include the platform and year dummies and their interaction terms, while (C) does
not include them. We add the interaction terms to account for product lifecycle effects that presumably
differ by console. 10 The chi-squared tests in the table reject the hypothesis that all the coefficients are
zero, indicating that the model explains the data moderately well. Note that the Tobit model identifies the
standard error of the regression residual, and the estimate is found significantly larger than 1 in both (B)
and (C).
The estimates of our interest are the coefficients of SPDP , SPSP , and SG. Result I finds that all
estimates are significantly different from zero. The estimates of the coefficients under (A) resemble those
under (B), indicating that ignoring the truncation in our data would not be a serious issue. The estimate of
the SG coefficient implies that the release dates of games in the same genre are more spaced out than those
classified into different genres. The estimated SPDP coefficient shows that games under the same ownership
are released more efficiently than those under different ownership, and indicates that multi-product publishers
avoid their games being cannibalized. The introduction dates of the same ownership are less congested by
approximately two days apart (i.e., we multiply the coefficient by the number of days per month).
An interesting observation is that the estimated coefficient of SPSP is twice as much larger than that
of SPDP , and the difference in the estimated coefficients is statistically significant. Even under the joint
ownership, platform providers have a better control over the release scheduling of their own games, com-
pared with non-platform providers. This finding is consistently explained by the industry practice of the
review process of video games.11 Platform providers have established the process in which product ana-
lysts scrutinize and screen games before approving them for publication on the console. This game-review9This is yet consistent with the information in Table 1. The number of titles in Table 1 counts the number of active titles,
and thus the same title is counted multiple times as long as it receive sales for the multiple years.10Due to multi-collinearity, we are able to include only eight interaction terms in the final estimation results.11The following discussion on the game authorization process is based on Kent (2003), and interviews with the people in
Taito, Co., a game publisher.
8
process usually comprises of two components: an approval for the content of game, and for the quality of
the master-rom, from which a game is produced on cartridges or discs. A publisher asks a platform provider
to review their game for publication, once the release date of the game is set. If the date is not planned
yet, the platform provider would not start the review, considering that the game under question is not ready
for marketing. The amount of time taken on this process varies from a couple of weeks to two months, and
the outcome of the process ranges from fail, revision, to pass. While platform providers have clear visibility
as to where their game stands in the review process, third party publishers have a difficulty predicting the
outcome of and the time taken on this review. As a result, many third-party publishers have to reschedule
their publication date of their game, because of delay in the review process. The comparison of the SPSP
and SPDP coefficients indicate that platform providers make more efficient release dates scheduling, because
they can predict the outcome of the review procedure more accurately.
The maintained assumption made in the above estimation model is that a publisher considers all the
possible pairs of games with equal weight in their decision of release timing. While it might not be conceivable
that publishers use an equal weight on all the game pairs, it is just impossible to know precisely which pairs of
games are considered most in the publisher’s release timing decision. We make an assumption alternative to
the equal-weight assumption to check the robustness of our previous findings. Here we assume that a publisher
correctly anticipate ex-post performance of games, and schedule the release dates based on this performance
measure. We use two performance measures in the analysis; life-time revenues and life period. We divide the
data based on each of the ex-post performance measures, and redo the empirical exercises. The lower part
of Table 5 shows the results. In the same way we did in Figure 1, we divide the distribution of each of the
performance measures at the 30th and 70th percentiles, and categorize the data into three segments.12 We
denote “H” the segment of top 30 percent , “M” the segment between 30 and 70 percents of the distribution,
and “L” the segment of bottom 30 percent. We make a pair of games in the combination of the three
segments, and estimated the Tobit model with the control variables on each combination independently.13
The results include games that are truncated from the right, however, excluding the truncated data does not
change our discussion below. Results II and III report for each combination of the segments the estimated
coefficients of SPDP , SPSP , SG, and the number of observations. 14
Results II and III in Table 5 report that, except for the coefficients of SPSP in the combination of (H,
H) and SG at (M, H) in Result II, the estimated coefficients have either the expected sign, or no statistical
significance. More than 66 percent of the combinations have the SPSP coefficients significantly different from
zero and larger than the corresponding SPDP coefficients. In most of the cases, the SPDP coefficients are
not significant, consistent with the reasoning that the effect of the game review process is more pronounced
in the estimation with the restricted sample. The estimated coefficient of SG is significantly positive for
more than 80 percent of the cases. The implications derived from the results are not as clear as those from
Result I, presumably because publishers may not focus on the ex-post performance measures precisely the12Alternatively we divided the distribution into four segments at the 25th, 50th, and 75th percentiles, and redo the estimation.
The results obtained from the three segments here qualitatively hold with the four-segment case.13Depending on the combination of (H, M, L), we cannot include some of the fixed effects terms because of the presence of
multicollinearity.14The pairs are made on the restricted sample based on the revenues and life period. Thus the number of observations used
in Result I does not add up to the total number of observations in either Results II or III.
9
way we analyzed in this section. Nevertheless, for the majority of the combinations, we confirmed our finding
in Table 1 that platform providers sparse the release dates of their games farther apart than non-platform
providers, and that games under the same genre are in close rivalry.
5 Conclusion
This paper analyzed the effect of ownership structure on the release dates scheduling of video games launched
in the U.S. in the period from 1994 to 2001. Our data contain cross-sectional (namely platform) and time-
series (year) dimension, and thus allow us to incorporate various fixed effects to control for console lifecycle
effects. Making a pair of games as a unit of observation, we found that publishers avoid cannibalization by
spacing out the release dates of their games. Furthermore, platform providers place the release dates farther
apart than non-platform providers do. We attributed this observation to the industry practice of game review
process evolved after the “Atari Shock.” This review process confers strong authority on platform providers
as to publication of game for the platform. Uncertainty concerning this process burdens on non-platform
publishers in predicting their game release dates. Indeed non-platform publishers are sometimes forced to
delay the release dates because of the outcome of the review process.
This paper also uncovered that the adoption pattern of video game sales are front-loading and L-shaped.
This point was known in the industry, but had not been confirmed empirically. The L-shaped adoption path
further ascertains the importance of the release dates scheduling as a strategic decision variable for game
publishers.
Several research topics are worth pursuing as an extension of this paper. We discuss two of them here.
One is on the micro-foundation of the L-shaped diffusion pattern described in Section 2.2. This paper
only tests an implication of this diffusion pattern, but does not analyze this diffusion pattern itself. We
discussed in the paper several elements that might be important to shape this diffusion pattern; word-of-
mouth communication, retail price level, and competition with other products. It would be an interesting
project to associate these elements in an integrated framework to improve our understanding of this particular
diffusion path.
Another interesting research topic is to extend our reduced-form estimation model to a structural frame-
work (Goettler and Shachar, 2001; Einav, 2003). For example, our reduced form analysis cannot uncover
the optimal release dates schedule that maximizes publisher’s profit. Simulating the optimal release timing
is beyond the scope of this paper, partly because it is not easy to analyze the market involved with many
publishers and games at the current state of structural estimation method. However, we believe that a
further sophistication of structural estimation method enables us to answer such a tough question in future.
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11
TABLE 1
Market Structure in the U.S. Video Game Software Market1994 - 2001
Platform Types (formats) Introduction Year Platform Providers 1994 1995 1996 1997 1998 1999 2000 2001
PlayStation September 1995 Sony % units sold 5.32 25.61 46.89 59.92 66.16 61.93 46.18(CD-ROM) % variety 0.83 10.17 23.79 37.66 49.06 58.16 53.36
% platform units 0.29 0.25 0.34 0.30 0.26 0.21 0.17% platform variety 0.21 0.18 0.18 0.19 0.19 0.17 0.15
N64 September 1996 Nintendo % units sold 8.62 24.50 26.85 24.71 25.23 14.03(Cartridge) % variety 0.13 1.69 6.31 13.43 18.90 16.12
% platform units 0.85 0.65 0.51 0.44 0.47 0.53% platform variety 0.78 0.45 0.25 0.18 0.19 0.20
Genesis September 1989 Sega % units sold 56.87 50.40 33.11 11.07 5.61 1.99 0.27 0.10(CD-ROM) % variety 41.33 46.23 42.38 32.67 21.00 13.44 5.38 4.76
% platform units 0.26 0.23 0.26 0.20 0.14 0.09 0.14 0.12% platform variety 0.26 0.26 0.28 0.31 0.31 0.26 0.22 0.15
Super Nintendo September 1991 Nintendo % units sold 35.35 38.18 24.98 12.35 6.17 2.66 0.42 0.19Entertainment System % variety 34.13 39.99 35.77 24.37 17.22 12.04 5.21 4.27(Cartridge) % platform units 0.26 0.30 0.43 0.30 0.29 0.30 0.23 0.19
% platform variety 0.05 0.06 0.08 0.13 0.18 0.24 0.38 0.20
PlayStation 2 October 2000 Sony % units sold 2.43 29.51(DVD-ROM) % variety 0.73 7.00
% platform units 0.01 0.10% platform variety 0.03 0.05
Dreamcast September 1999 Sega % units sold 4.22 9.70 9.99(CD-ROM) % variety 0.93 8.54 13.28
% platform units 0.47 0.43 0.43% platform variety 0.25 0.20 0.19
Saturn May 1995 Sega % units sold 2.12 6.85 5.12 1.45 0.25 0.02 0.003(CD-ROM) % variety 0.97 8.40 16.33 17.57 11.08 3.08 1.21
% platform units 0.60 0.39 0.27 0.32 0.31 0.30 0.14% platform variety 0.47 0.31 0.26 0.30 0.34 0.33 0.26
Nintendo Entertainment January 1986 Nintendo % units sold 7.78 3.98 0.83 0.07 0.003 0.000System % variety 24.54 11.98 3.15 1.15 0.24 0.014(Cartridge) % platform units 0.51 0.62 0.80 0.81 0.86 0
% platform variety 0.10 0.15 0.32 0.42 0.47 0Total units sold (Million) 39.13 38.89 46.02 74.37 110.55 126.01 118.66 112.38
Total No. variety 1234 1436 1480 1518 1494 1514 1678 1945
Note: The platforms are in order of the total units sales in the period of 1994-2002. The eight platforms covered 99.4 % of the U.S. home video game market.
TABLE 2
Description Statistics of the U.S. Video Game Software Market1994 - 2002
Top 10 publishers (% titles) Top 10 publishers (% revenues) Top 10 Games (platform) Rev (USD million)SEGA 8.42 NINTENDO OF AMERICA 16.92 SUPER MARIO (N64) 287.35ELECTRONIC ARTS 6.55 SONY 11.80 GOLDENEYE 007 (N64) 246.10SONY 5.75 ELECTRONIC ARTS 10.97 ZELDA: OCARINA TIME (N64) 192.80ACCLAIM 5.53 SEGA 5.12 MARIO KART (N64) 188.13INFOGRAMES 3.79 THQ 4.89 GRAND THEFT AUTO 3 (PS2) 143.18KONAMI 3.71 ACCLAIM 4.81 POKEMON STADIUM (N64) 132.87NINTENDO OF AMERICA 3.51 CAPCOM 2.96 DONKEY KONG COUNTRY (SNES) 131.75MIDWAY 3.41 MIDWAY 2.81 DONKEY KONG (N64) 130.83CAPCOM 3.21 ACTIVISION 2.65 SUPER SMASH BROTHERS (N64) 118.47THQ 3.09 NAMCO 2.64 GRAN TURISMO RACING (PS) 117.38No. Titles in the industry 4015 Total Industry Revenue USD 21.7 b
multi-platform titles 31.61Monthly Units Sales (%)
January 6.6February 6.2
March 7.5April 5.0May 4.1June 5.6July 4.5
August 4.7September 7.2
October 6.4November 12.0December 30.3
1 year 2 year 3 year 4 year 5 year 6 year 7 year
PS 34.21 22.08 18.38 16.33 14.57 13.65 12.57
(10.78) (7.78) (6.67) (5.85) (5.26) (4.74) (7.23)
N64 45.25 29.79 23.94 22.57 24.84 30.00
(13.53) (12.48) (10.08) (9.80) (10.07) (8.28)
GE 39.67 24.25 17.31 12.90 10.09 9.18 7.86
(13.59) (9.39) (7.55) (6.23) (5.41) (4.24) (4.44)
SNES 43.51 28.46 23.32 18.09 15.69 13.55 10.45
(13.71) (11.05) (9.76) (8.51) (7.35) (5.31) (4.23)
PS2 41.71 27.60
(7.96) (8.30)
DR 30.83 17.00 13.33
(8.93) (6.74) (4.80)
SA 40.04 20.08 12.37 7.85 4.63 3.74 2.85
(11.22) (8.12) (4.96) (3.73) (2.97) (2.31) (0.48)
NES 23.71 16.35 15.91 11.11 10.07
(9.92) (7.33) (7.00) (5.68) (3.96)
Note: Inside parentheis is standard error.
TABLE 3
Average Game Price by Console and by Age
TABLE 4
Summary Statistics on Major Variables
Variables Descriptions Mean Std. Error Min Max
GAPi,j,g Gap in the release dates between two games, i and j on platform g 0.73 1.14 0 4
SPDPi,j,g 0-1 Dummy that takes 1 if games i and j are published for 0.039 0.194 0 1platform g by the same publisher that is not a platform provider.
SPSPi,j,g 0-1 Dummy that takes 1 if games i and j are published for 0.035 0.184 0 1platform g by the same publisher that is also a platform provider.
Sgi,j,g 0-1 Dummy that takes 1 if games i and j are in the 0.36 0.48 0 1 same genre for platform g.
No Observations: 283211
Control Variables:
Sample means of year dummies Sample means of console dummies1994 0.83 PS 0.101995 0.02 PS2 0.021996 0.02 Genesis 0.401997 0.02 Saturn 0.011998 0.02 DreamCast 0.011999 0.02 NES 0.212000 0.04 SNES 0.242001 0.03 N64 0.01
TABLE 5
Estimation Results on Effect on Release Timing
Result I. Pooled Regression
Est Std Err Est Std Err Est Std Err
0.06 a 0.01 0.06 a 0.02 0.05 a 0.020.12 a 0.01 0.12 a 0.02 0.08 a 0.020.06 a 0.01 0.06 a 0.01 0.02 a 0.01
- - 1.57 a 0.002 1.57 a 0.002
Test of Joint significance (Х2 test)
Test of SPDP = SPSP
Result II. Tobit Regression by Revenues Result III. Tobit Regression by Life-years
Est Std Err Est Std Err Est Std Err Est Std Err Est Std Err Est Std Err
L SPDP 0.04 b 0.02 0.17 a 0.05 0.35 a 0.10 L SPDP -0.02 0.05 -0.06 0.04 0.10 0.08SPSP 0.08 a 0.03 0.43 a 0.04 0.12 0.09 SPSP -0.11 0.08 0.62 a 0.06 0.58 a 0.05
SG 0.07 a 0.01 0.29 a 0.02 0.14 a 0.04 SG 0.11 a 0.03 0.09 a 0.03 0.07 b 0.03# Obs # Obs
M SPDP 0.01 0.05 0.06 0.10 M SPDP 0.05 0.03 0.13 a 0.05SPSP 0.25 a 0.06 -0.20 0.10 SPSP 0.12 a 0.05 0.16 a 0.04
SG 0.08 a 0.03 -0.14 a 0.05 SG 0.06 a 0.02 0.19 a 0.02# Obs # Obs
H SPDP -0.13 0.09 H SPDP 0.00 0.05SPSP -0.37 a 0.10 SPSP 0.11 a 0.04
SG -0.10 0.06 SG -0.02 0.02# Obs # Obs
NotesThe variable names are listed in Table 4. The dependent variable is GAP. The subscripts a and b indicate the 99-percent and the 95-percent significance levels, respectively.For Results II and III, we divide the data in terms of an ex-post performance measure of games: lifetime revenue for Result II, and years of life period for Result III. We divide the distribution of each of the performance measures at the 30th and 70th percentiles, and categorize the data into three segments: H (top 30% of the distribution), M (30-70%), and L (bottom 30%). We make a pair of games in the combination of the three segments, and estimated the Tobit model with the fixed effects on each combination independently.
45.09 (3) a906.8 (25) a
SPDPSPSP
SGStd Error
Platform / Year Y Y N
OLS Tobit Tobit( C )( B )( A )
M HL M H
11056
12234
10646
L
60008 36179
96015
31594
Fixed Effects
40668
39581
66.98 (25) a
13.26 (3) a 9.66 (3) a 3.27 (3) b
28451 29368 14877
FIGURE 1Unit Sales per Game Title over lifecycle Classified by Life Duration, 1994-2002
0
5
10
15
20
25
30
0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96
months elapsed since launch
1000 units
1 years or shorter (13.85%)
1 - 2 years (23.32%)
2 - 4 years (36.89%)
4 years or longer (29.89%)
Total Number of Games = 4015
The share of the number of game
titles is in parenthesis.
