Games PS2 Gta San Andreas MAP Manual
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|sarath1975||11:46am on Sunday, October 10th, 2010|
|There was so much hype surrounding Grand Theft Auto: San Andreas I decided I had to get it to see what all the fuss was about.|
|blastrax||9:53pm on Thursday, September 9th, 2010|
|Codes 4 grand theft auto. are you selling it its an awesom game realy addicting not enough mods for cars and cars to mod and some levels are a little iratating oh yeah and no SEX|
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Blockbuster Software Titles and Indirect Network Eects in the Home Video Game Industry
Raymond Lee January 16, 2009
This paper investigates the indirect network eects in hardware/software system markets. It is argued in the previous literature that the sales of the hardware is highly aected by the availability of compatible software products. A majority of the previous literature has shown that software quantity is a major factor inuencing hardware demand. By doing so, the eect on the hardware demand is assumed to be equal across software titles. (Nair, Chintagunta, and Dub 2004) In this paper, I use a static model in which I let individual software titles e vary in how much they aect hardware demand. Dierent from the literature that also does this(Lee 2008), I allow software titles to be substitutes(Derdenger 2008). I show that for the 6th generation video games and consoles that I use in my empirical analysis, one blockbuster game can impact sales of other games on the same console. Then I test for my data that high quality software titles have a bigger impact on hardware demand then do low quality software titles. Further, I argue that a hardware platform needs a blockbuster title to attract consumers.
Many high tech industries provide products that are usable in conjunction with a compatible platform. These are referred to as hardware/software markets. Examples of such systems are video console/game, mobile phone/service provider, and computer OS/software. The consumer demand for platforms depend on the software compatible with the hardware. Thus hardware suppliers need to form a good pool of compatible software in order to attract more consumers. Having a good pool of software includes meeting various needs of consumers as well as attracting mass demand with high quality software. In this paper, I examine the US video game market to discuss the importance of high quality software. The importance of software variety for hardware platform markets has been investigated in the previous literature. Nair, Chintagunta, and Dub (2004) and Clements and Ohashi(2005) e show the importance of having a competitive number of compatible software titles. In the fth generation video game market(1995-2002), Sony, although a new entrant to the market, penetrated the market by securing a wide variety of software titles so that they could market PlayStation to a wider consumer base. Sony eventually became a big player in the market. Sega, in contrast, failed to provide enough software variety and dropped out of the market. While the number of software titles has an important impact on hardware demand, the importance of having some specic high quality software titles is becoming more and more important. The importance of a high quality blockbuster title appeared in the sixth generation video game market(2000-2006). Sony maintained its market power in this generation through its early entry of PlayStation 2. PlayStation 2 provided backward compatibility of PlayStation games and also attracted game developers to form a wide variety of software. Sonys main competitors in the generation are Microsofts XBOX, and Nintendos GameCube which were launched a year after PlayStation 2. XBOX had features superior to PlayStation 2, but struggled to gain market share because
it was not able to attract game developers. However, the launch of Microsofts own developed and exclusive title Halo and its sequel Halo 2 was able to attract consumers to XBOX. By October 2006, about 29.8% and 36.8% of XBOX owners had bought Halo and Halo 2 respectively. GameCube lacked such a blockbuster title, and as a result, gained a lower share than XBOX. This is also partly because GameCube was targeted only to loyal and young consumers. Lee(2008) and Derdenger(2008) examine this generation and provide frameworks that allow software titles vary in how much they aect hardware demand. In the following, I will describe some aspects of the data used, and predict demand systems for hardware and software markets of the sixth generation video game market. This paper diers from Lee(2008) in that I assume software titles to be substitutes within platform. Unlike Derdenger(2008) where software utility is calculated directly from observed data without estimation, I estimate software demand as well. This allows us to separate how software titles aect hardware demand from other factors such as seasonal eect. I also conduct counterfactual experiments to investigate the eect of blockbuster software title.
The data consists of monthly sales and average prices of hardware and software that is provided NPD group. While hardware sales and prices, and software sales data are available from January 2001 to October 2006, software price data are only available from May 2004 to October 2006. Thus I will use data from only this period. Software attributes data including GameSpot review score, critic average review score, user average review score, title release date, ESRB rating, and genre are gathered from GameSpots web site, www.gamespot.com. The collected review scores will be used as a proxy for consumers software quality perception. I focus on the three main platforms, GameCube, PlayStation 2, and XBOX. From the data described above we have three platforms and 1596 software titles with full information over 3
Age of title 14
Price($) Mean SD 38.495 11.542 37.546 11.583 36.589 11.822 35.019 11.723 32.995 11.370 30.762 11.170 28.837 10.761 26.965 10.449 25.644 10.394 24.470 10.220 23.239 9.835 22.131 9.140 21.119 8.411 20.400 7.926 19.981 7.812
Unit Sales Mean SD 53599.504 159168.553 45722.028 99955.724 21708.663 57194.179 17255.550 35046.699 11612.810 41441.099 8508.927 17038.846 7131.245 12702.645 6293.121 9628.653 6088.471 13942.524 6157.226 9236.668 5534.699 9053.562 4766.507 7949.223 4993.864 9348.809 6405.855 14836.135 4845.288 10767.053
Table 1: Mean prices and sales of software titles across age of game. the period of May 2004 to October 2006. Table 1 shows the mean of price and sales over all games by age. We can see there is a general decreasing trend in both price and sales. This is due to market saturation, coming from the fact that consumers purchase a software titles only once and tend to exit from the potential market of that specic title.(Nair 2007) A similar pattern is observed in the hardware price and sales except that hardware prices drop interruptedly at certain periods when hardware providers cut store tagged price. Seasonality, like in many other markets is evident in the data. Figure 1 shows that the total sales over the period of both hardware and software are signicantly high in December. Sales of software titles are concentrated with a few successful blockbuster games. The top 14 games, which is only 0.58% of the total number of games, accounts for 10% of the total sales of software titles. This phenomenon is most apparent for XBOX. The biggest blockbuster of XBOX, Halo and its sequel Halo 2 together account for 8% of the game sales compatible
Figure 1: Total sales per month.
with XBOX. This motivates the existence of blockbuster eect in software/hardware system markets. Such high quality software titles negatively aect sales of competitors and positively aect sales of the corresponding platform. A reduced form analysis examining the competitive aspect between software titles is provided in section 3.1.1. The May 2004 to October 2006 period includes introduction of two major blockbuster titles. Grand Theft Auto: San Andreas for PlayStation 2 in October 2004 and Halo 2 for XBOX in November 2004. These two software titles record unit sales over two times the next best exclusive title, and over ve times Paper Mario: Thousand, the biggest introduction for GameCube in the period. The period of the data having dominant and compatible blockbusters for only two of the platforms sheds the potential of identifying the blockbuster eect.
6 x106 (hw)
6 x107 (sw)
Video Game Demand
For the video game demand, the consumers utility for game k in month t given that having purchased console j is
SW SW SW uSW = P Pkt + Xkt SW + kt + ikt ikt
Pkt is the price of game k, Xkt is a vector of game characteristics, kt is the mean unobserved characteristics of software k common over individuals at time t, and ikt is the individual deviation from the mean utility. Note that the outside goods utility is set to have mean zero. Popular video games often become published in alternative editions. Also, games requiring additional accessories for game play are available as an alternative bundle with the accessory. The share of these software titles are likely to be under-counted because the sales get split across these alternative editions. For this reason, in estimation, I add the sales of all available packages for a software title, and use the average of the package prices weighted by sales. Game characteristics include game specic xed eects for all software titles which enables me to capture all time invariant software characteristics, and the CPI adjusted average sales price. Platform xed eects are included to let identical games have dierent sales levels on each platform, because the games are facing dierent consumers. I also include a nonlinear function of game age in order to capture market saturation and other temporal phenomena such as nonlinear patterns associated with a new release. There is a possibility of game age reecting a partially positive eect on demand such as awareness of word-of-mouth, but this is likely dominated by the saturation eect given the general trend of decreasing price and
sales. I assume that ikt , the individual taste follows an independent type-I extreme-value distribution. Then the market share of software k is given by
SW SW SW exp(P Pkt + Xkt SW + kt ) skt = SW SW SW 1 + lKj exp(P Plt + Xlt SW + lt )
Consequently, following Berry(1994), we get a linear regression model as skt s0t
SW SW SW = P Pkt + Xkt SW + kt
I am assuming here that consumers make discrete choice so that they purchase only one software title in a period. This simplies the denition of s0t and allows us to write equation (2). In dening the market size, mt , I use the installed base of platform j at time t. Thus I am assuming that a consumer can repurchase the software title with some probability. This is a strong assumption and requires relaxation due to the fact that video games are durable goods. However, inclusion of the nonlinear function of video game age will partly capture the relative change in market size across the various software titles. The demand for software product k is qkt = mt skt where mt is the market size at time t.
Competitive Aspect of the video game market
To assert the competitive nature of video games, I briey examine the eect of a blockbuster game introduction. In particular, I consider the eect of the introduction of the best-selling
Playstation 2 action game Grand Theft Auto: San Andreas. I examine how the sales and prices of software titles competing to the blockbuster are aected. As a reduced form approach, I do OLS regressions of sales and price of software titles sharing genre with Grand Theft Auto: San Andreas on a dummy variable indicating that a title is introduced at the same period October 2004, as the blockbuster title. This dummy variable will capture the eect of the introduction of Grand Theft Auto: San Andreas on competing software titles. I also control for age and seasonal eects. I repeat same for the top selling sports game Madden NFL 2005 that was introduced at August 2004. The results are given in table 2. We can see that both the sales and price of competitors of Grand Theft Auto: San Andreas are lower than the overall average. For Madden NFL 2005, competitors have sales signicantly lower with no signicant dierence in price. Thus we can say that competitors of a high quality software title are negatively aected if introduced in the same period as the high quality title. The control variables all give coecients in the expected direction.
Intercept Age Holiday Entry on Oct. 2004
Estimate 15867.965 -432.985 18010.980 -7371.364
Unit Sales Std. Error 544.2460 19.5244 1175.7239 1690.3899
t-statistic 29.1559 -22.1766 15.3191 -4.3607
Estimate 28.0120 -0.3272 -0.0743 -3.6553
Price Std. Error 0.1768 0.0063 0.3820 0.5492 Price Std. Error 0.1066 0.0034 0.2220 0.7941
t-statistic 158.4106 -51.5772 -0.1944 -6.6554
Intercept Age Holiday Entry on Aug. 2004
Unit Sales Estimate Std. Error 15734.8482 363.7391 -401.0932 11.4584 14405.2604 757.3136 -6286.6267 2709.3624
t-statistic 43.2586 -35.0043 19.0215 -2.3203
Estimate 28.5455 -0.3193 0.2281 0.7427
t-statistic 267.7595 -95.0739 1.0275 0.9352
Table 2: OLS regressions to test changes in sales and price of games introduced at the same period as a blockbuster game. Top is for action games against Grand Theft Auto: San Andreas, bottom is for sports games against Madden NFL 2005. 8
Game Console Demand
For the game console demand, the consumers utility for console j in month t is
HW HW HW uHW = P Pjt + Xjt HW + SW Ujt + jt + ijt
Ujt = log(
exp(kt )) u
Pjt is the price of console j, Xjt is a vector of console characteristics, jt is the mean unobserved characteristics of console j at time t common over individuals, and
is the individual
deviation from the mean utility. Ujt is a measure of utility from software titles available at time t. Unlike Derdenger(2008), where software demand estimation is omitted and the observed shares are used to calculate software utility, I use the tted utility of individual games from the previous software demand system. By doing so, I am able to separate the eect of software availability from the holiday eect. I use ukt which is the expectation of ukt with the seasonal eect eliminated. Without the separation, Ujt becomes highly aected by the seasonal eect, making it hard to distinguish the software eect from seasonal eect. While any additional software will give a positive eect on console demand, software utility are negative for all games because it is normalized so that the outside good gives zero utility. I take the exponential of software utility and sum up the exponential of utility available for each console each month to get the software availability measure, and again take the logarithm to get Ujt. Since I use the software utility through the demand system in the previous section, software titles have heterogeneous and time variant eect on the hardware demand. The console characteristics such as processor speed, CPU bits, or graphics quality do not change over time, so I use console specic dummy variables to capture the time invariant characteristic eects of each hardware system. I also include console age and seasonal eect variables to capture time dependent eects.
As in the video game demand, I assume
to follow an independent type-I extreme-value
distribution. This leads to the following model similar to the video game demand model. Sjt S0t
HW HW HW = P Pjt + Xjt HW + SW Ujt + jt
Skt is the share of hardware console j in time t. The demand for hardware console j in time t is
Qjt = Mt Skt
where Mt is the market size at time t. I follow Derdenger(2008) to determine the market size, where the predicted initial market size is M = 78, 354, 700 and the market size at time t, Mt , is Mt = M (cumulative console sales). By this I implicitly assume that any consumer exits the market along with purchase.
I estimated the game demand equation using a conditional moment with kt , the unobserved characteristics. The condition is E(|Z) = 0, where Z is a set of instruments uncorrelated to. For software price, the use of commonly used instruments is likely inappropriate because marginal cost is constant, regional data is not available, and software attributes explain little of the pricing process.(Nair 2007) In belief that games at similar quality levels and being in the same genre will share similar pricing patterns, I use game software development PPI interacted with GameSpot review score and genre xed eects. In estimation for the console market, I again use instrumental variables to handle the correlation of variables with the demand shock. Instruments used for price, following Liu(2007), are PPI for computers and computer storage devices. For the software availability, I use the 10
logarithm of the number of compatible software available in the market for each platform each period.
I rst examine the game specic xed eects. Table 3 shows an OLS regression of the game specic xed eects on the game characteristics, genre and ESRB rating. This shows how much of the software demand is explained by the time invariant software attributes. None of the genres are signicantly higher than the base genre action, while Adventure, Flight, Racing, Sports, and Strategy are lower. For the ESRB rating, we can see that Everyone 10+ and Mature have higher eects on the game specic xed eects. GameSpot review score has a signicantly positive eect providing evidence that higher quality leads to higher demand. These xed eects only account for an R2 of 0.1355 on the game specic xed eects. Table 4 shows the software demand coecient estimates from OLS and GMM. All estimates are signicant in the intended direction. Platform xed eects are included to control for each market. Since Playstation 2 has more games, relative demand of software titles is measured lower than the two other platform markets. We can see that the price coecient is signicantly negative as expected. The nonlinear function of game age signicantly captures the decreasing trend of a software title over time agreeing with the market saturation argument.(Nair 2007) Seasonal eect is shown to be evidently strong.
Intercept Adventure Arcade Childrens Family Fighting Flight Other Racing Role-Playing Shooter Sports Strategy Everyone 10+ Mature Teen GameSpot
Estimate -12.1929 -0.7288 0.2619 0.1855 0.2139 0.1343 -0.7802 0.5422 -0.6928 -0.2217 -0.1446 -0.8006 -0.7824 2.2951 0.8121 0.1675 0.3080
Std. Error 0.2713 0.2321 0.4845 0.8291 0.2462 0.2218 0.3710 0.8238 0.1840 0.2000 0.1876 0.1717 0.3064 0.2704 0.1737 0.1358 0.0345
t-statistic -44.9347 -3.1403 0.5406 0.2237 0.8686 0.6053 -2.1032 0.6582 -3.7648 -1.1087 -0.7709 -4.6619 -2.5538 8.4865 4.6763 1.2329 8.9322
Table 3: OLS of game xed eects on Genre and ESRB. Base is Action and Everyone. GameSpot is for GameSpot review score.
GameCube PS2 XBOX Price Age Age2 log(Age+1) Holiday
OLS Estimate Std. Error t-statistic 2.3304 0.0406 57.4490 2.1839 0.0396 55.1816 2.2896 0.0391 58.5038 -0.0186 0.0006 -29.6849 -0.0112 0.0028 -4.0119 0.0000 0.0000 0.0620 -0.5846 0.0227 -25.7906 2.1354 0.0200 106.6941
2SLS Estimate Std. Error 3.1124 0.0892 2.9655 0.0888 3.0431 0.0860 -0.0367 0.0019 -0.0140 0.0028 0.0001 0.0000 -0.7179 0.0265 2.1381 0.0201
t-statistic 34.8740 33.4084 35.3769 -18.8838 -4.9680 1.9287 -27.1001 106.3268
Table 4: Software demand coecient estimates. Holiday is a dummy variable for December. Game specic xed eects not reported.
Table 5 reports the coecient estimates for the console demand system. All estimates are signicant in the intended direction. PlayStation 2 has the highest demand, followed by XBOX. Price has a signicantly negative estimate. The software utility coecient is signicantly positive showing that the indirect utility coming from software availability is a big factor in hardware demand. The seasonal eect is also signicant. The combination of age eect is negative since the ages of consoles are over 30 months in the data set. This again can be explained by market saturation.
GameCube PS2 XBOX Price Software Holiday Age Age2
Estimate -1.2427 3.5488 2.3701 -0.0548 0.1400 1.9715 0.0694 -0.0012
OLS Std. Error t-statistic 1.1213 -1.1082 1.3500 2.6288 1.3609 1.7416 0.0093 -5.9073 0.1857 0.7542 0.2316 8.5116 0.0584 1.1882 0.0006 -1.8565
2SLS Estimate Std. Error t-statistic -0.6590 1.0102 -0.6523 6.7950 1.3882 4.8947 5.6995 1.4053 4.0556 -0.0921 0.0115 -8.0375 0.4823 0.1860 2.5934 2.0262 0.2072 9.7788 0.2166 0.0608 3.5611 -0.0029 0.0007 -4.2880
Table 5: Hardware demand coecient estimates from OLS and 2SLS.
In this section, I use the demand estimates to conduct counterfactual experiments that capture the heterogeneous eect of software titles, especially, blockbuster games. I do this by switching software attributes, calculating software utility using demand parameter estimates, and forming the new software utility index that aects hardware demand. After retrieving new hardware share, I multiply the market size to get new hardware sales. This in turn updates the installed base for the next period. Thus I observe the eect of software attribute change on installed base. Note that this is only partially counterfactual because I take software title prices unchanged from the original data, while it is expected to be correlated with other newly adjusted software attributes.
Equal xed eects
The software attribute that I experiment with is the game specic xed eects obtained from the software demand system. Table 6 gives descriptive statistics of the xed eects. First I x all of the game xed eects for each platform at an equal level. This will upgrade the eect of games at the low end, and downgrade the eect of those at the high end. Figure 2 shows how installed base changes over time when all game xed eects are xed at mean level. We can see that the sales decreases dramatically. The decreased sales are 3,351,847, 9,272,720, and 3,853,845 units respectively for GameCube, PlayStation 2, and XBOX. This implies that the utility loss from high end titles is higher than the gain from low end titles. GameCube PS2 XBOX Min. 1st Quar. -15.47 -11.26 -15.85 -11.85 -14.79 -10.88 Median Mean -9.578 -9.584 -10.070 -10.220 -9.493 -9.555 3rd Quar. Max. -7.954 -4.762 -8.534 -4.607 -8.212 -4.607
Table 6: Descriptive statistics of the game specic xed eects
I manually searched for xed values, -8.28, -8.91, -8.66, that give hardware installed base close to reality. These are about the 31st, 31st, and 33th percentiles of the game specic xed eects within each platforms. This supports the argument that high quality software have higher eects on hardware demand. The results are shown in Figure 3.
The previous section shows some evidence that high quality titles have larger eects on the hardware sales. In this section, I investigate the eect of specic blockbuster titles. I examine the two major blockbuster introductions in the period, Grand Theft Auto: San Andreas for PS2, and Halo 2 for XBOX. Both of these titles are sequels of blockbuster games. Grand Theft Auto: San Andreas and Halo 2 sold 6,510,244 units and 5,323,384 units respectively during the time period. These correspond to 18.7% and 36.8% of the installed base of PS2 and XBOX. First I examine the result of downgrading the quality of Grand Theft Auto: San Andreas of PS2. I do this by setting the titles game specic xed eect at the mean level. Figure 4 shows the result. We can see a decrease in the PS2 installed base that XBOX and GameCube takes away. PS2 loses 377,104 unit sales with Grand Theft Auto: San Andreas absent. XBOX gains 26,034 unit sales and GameCube attracts 41,111 consumers. Now I examine the opposite case, Halo 2 being downgraded. Figure 5 shows the result. XBOX loses 472,886 unit sales. PS2 gains 132,507 unit sales and GameCube attracts 42191 consumers. The loss of unit sales for PlayStation 2 and XBOX in the two experiments account for 3.31% and 8.17% of the total reality sales. Considering the loss is from losing a single software title, this evidently shows the strong impact of blockbuster titles.
Now I try adding median quality software titles to XBOX after degrading the quality of Halo 2, to see how many titles are needed to overcome the loss of a blockbuster. Manual search using the model suggests that hardware sales of an equal level can be reached with 22-23 more median level software titles. The total number of software titles for XBOX in the whole data set is 800, and the number available at the introduction of Halo 2 is 496. This means that XBOX needs to introduce 7.57% more games than reality in this period, to reach the level of real hardware sales according to the model.
In this paper we examined indirect networks eect in software/hardware system markets. We nd by using GameSpot review scores as a proxy, that software quality leads to higher demand of a software title. It is also discussed that the software titles market has a competitive nature in that an introduction of a blockbuster title negatively aects sales and prices of competitors. High software quality leads to higher hardware demand by raising software availability related utility. Counterfactual experiments show that high quality, or blockbuster, software titles have stronger impacts on the hardware demand than average titles. The implication of this is that it is more important having software with dominant impact than having many mediocre software titles, for a hardware platform to attract more consumers. The used model has limitations in sense that it does not account for consumer heterogeneity. An extended model with random coecients or latent-class will be needed to overcome this limitation. Also, the software market size denition does not suitably account for market saturation. Improvement on these concerns are left for future research. Investigating the eect of having special editions is another issue to be studied in the future.
Special, limited, or collectors editions normally have about the same quality for the software itself, but comes with good packages or some small exclusive additions to the software title. These types of editions are usually available for blockbuster titles, and the proportion of sales of the edition likely depends more on the quality than the additional price. Future research on the special edition eect will likely nd evidence for additional prots in the strategy. Setting up a detailed framework on the competitive software market incorporating eects of software ownership will be a direction for this.
Berry, Steven T. (1994). Estimating Discrete-Choice Models of Product Dierentiation, RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer Clements, Matthew T. and Ohashi, Hiroshi,(2005) Indirect Network Eects and the Product Cycle: Video Games in the U.S., 1994-2002. Journal of Industrial Economics, Vol. 53, No. 4, pp. 515-542, Derdenger, T. (2008), Quantifying the Eects of Vertical Integration on Platform Pricing: Application to the Video Game Industry. mimeo Lee, R.,(2008) Vertical Integration and Exclusivity in Platform and Two-Sided Markets mimeo Liu, H.,(2007) Dynamics of Pricing in the Video Game Console Market: Skimming or Penetration? mimeo Nair, H., Chintagunta, P. and Dub, J-P. (2004). Empirical Analysis of Indirect e Network Eects in the Market for Personal Digital Assistants, Quantitative Marketing and Economics 2(1), 23-58 Nair, H., (2006) Intertemporal Price Discrimination with Forward-Looking Consumers: Application to the US Market for Console Video-Games. Stanford University Graduate School of Business Research Paper No. 1947
Figure 2: Simulated installed base scenario 1: All games xed at mean level. Solid and dotted lines are actual and simulated installed bases respectively.
Figure 3: Simulated installed base scenario 2: Hardware sales reaches real sales level when games xed at 30 33th percentile quality. Solid and dotted lines are actual and simulated installed bases respectively.
Figure 4: Simulated installed base scenario 3: PlayStation 2s hit Grand Theft Auto: San Andreas degraded. Solid and dotted lines are actual and simulated installed bases respectively.
Figure 5: Simulated installed base scenario 4: XBOXs hit Halo 2 degraded. Solid and dotted lines are actual and simulated installed bases respectively.
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