In the model above one of the main assumptions I implement is in regard to software compe- tition. I make the assumption that video games do compete with one another rather than assume games are monopolists like the previous works of Nair (2007) and Lee (2010). In or- der to validate this assumption I present the results of two tests below. The …rst determines whether cross price e¤ects are present with the implementation of a nested logit model while the second, tests whether falling prices are a consequence of competitive conditions with a simple price regression.
In determining whether there are cross price e¤ects among software titles I implement a nested logit model for software demand. However, under such model there are several concerns. One concern is that cross-price substitution might be under estimated if game developers strategically release video games as to minimize the cannibalization of similar games currently in the market. I follow a similar speci…cation to that of Einav (2006) and Nair (2007) which tries to account for this endogeneity with a nested logit model with nests corresponding to the video game genre. I also include a covariate which captures video game age. The video game demand speci…cation is:
ln(skjt=s0jt) = j+ (t rkj) + pkjt+ ln(skjtjg) + ln(N um
SW
t ) + kjt
wheret indexes month,rkj is the release date of gamekj,pkjt is the price, skjt is the market
share, s0jt is the outside good’s share, skjtjg is the within genre share of game kj in period t
and ln(N umSW)is the log of the total number of available games on platformj. Moreover,
the parameter captures the degree of correlation of utilities among games in a given genre. A small near zero infers little correlation among genre games while a larger value indicates larger cross-price e¤ects. Thus, a test of competition among software titles would be to determine if is statistically di¤erent from zero. Nonetheless, to properly test whether is statistically di¤erent from zero we need to account for the endogeneity of price, release timing
18See Lee (2010)
19The construction of the potential market size re‡ects the idea that a consumer is a …rst time buyer and
does not re-enter the market to purchase additional goods. Consequently, I do not account for multihoming consumers.
and within genre share. To correct for software price I employ the same price instruments as the main model. The endogeneity of release time is addressed with the inclusion of software …xed e¤ects. "With the inclusion of such all variation in demand arising from aspects of game-quality is controlled for." (Nair 2007) Lastly, the number of video games in a given genre in a given period instruments for within genre share. The results of several models are presented below including OLS and 2SLS with and without including instruments for price. I additionally include speci…cations with quadratic and cubic software age covariates. From the results it is clearly evident that video games compete against one another and are not monopolists.
Table 11: Competitive Software Tests
O L S 2 S L S w / In s tru m e nt s fo r p ric e & w ith in s h a re C o e ¤ S t d E rr. C o e ¤ S td E rr. C o e ¤ S td E rr. C o e ¤ S t d E rr. C o e ¤ S td E rr. C o e ¤ S td E rr. P ric e -0 .0 0 3 3 0 .0 0 0 3 -0 .0 0 5 9 0 .0 0 0 3 -0 .0 0 7 3 0 .0 0 0 4 -0 .0 1 1 8 0 .0 0 2 4 -0 .0 4 0 6 0 .0 0 5 2 -0 .0 4 4 6 0 .0 0 4 6 0 .8 4 6 1 0 .0 0 2 4 0 .8 3 8 4 0 .0 0 2 5 0 .8 3 4 5 0 .0 0 2 5 0 .4 2 9 5 0 .0 1 8 0 0 .5 4 7 6 0 .0 1 6 8 0 .5 3 9 2 0 .0 1 6 5 A g e -0 .0 3 6 3 0 .0 0 0 7 -0 .0 5 0 6 0 .0 0 1 2 -0 .0 6 6 9 0 .0 0 1 9 -0 .0 7 7 7 0 .0 0 2 2 -0 .1 4 0 8 0 .0 0 7 5 -0 .2 0 4 5 0 .0 1 0 8 A g e ^ 2 0 .0 0 0 3 2 .1 5 5 e -0 5 0 .0 0 1 2 8 .8 4 1 e -0 5 0 .0 0 1 4 0 .0 0 0 1 0 .0 0 5 3 0 .0 0 0 3 A g e ^ 3 -1 .5 0 3 e -0 5 1 .3 6 4 e -0 6 -6 .1 6 8 e -0 5 4 .7 1 4 e -0 6
If the results from the …rst test are not conclusive enough I present a second test to illustrate that software video game prices largely decline due to increased video game com- petition. For this test I pool all game data across each console and regress software price on age, game …xed e¤ects and the interaction of age and console speci…c month …xed e¤ects. I hence measure the rate at which prices fall after controlling for game quality via game …xed e¤ects. Negative and statistically signi…cant estimates of the interaction terms therefore indicate that prices fall due to the competitive interaction of software titles. In addition to this test I also employ a regression which implements the change in software prices each period as the dependent variable–positive and signi…cant estimates of the interaction terms will indicate competition impacts the rate of decline in software prices. The table below presents these results but only report the coe¢ cients of the interaction term for the …rst twelve months for space concerns.
Table 12: Competitive Software Test 2
P ric e G a m e C u b e P lay S ta tio n 2 X b ox C o e ¤ S t d E rr. C o e ¤ S td E rr. C o e ¤ S td E rr. A g e * J a n 0 2 -5 .4 5 2 9 1 .0 2 2 2 -1 .6 6 5 3 0 .0 5 4 7 -3 .0 8 3 2 0 .7 2 5 8 A g e * Fe b 0 2 -3 .6 2 2 0 0 .5 7 8 6 -1 .4 6 6 6 0 .0 5 0 1 -1 .6 5 3 2 0 .4 2 3 0 A g e * M a r 0 2 -3 .1 8 2 7 0 .4 0 9 7 -1 .4 2 7 3 0 .0 4 6 4 -1 .4 5 1 3 0 .3 0 2 9 A g e * A p r 0 2 -3 .5 6 3 0 0 .3 0 3 4 -1 .5 1 5 3 0 .0 4 2 8 -1 .8 2 7 8 0 .2 2 6 8 A g e * M ay 0 2 -3 .5 8 7 5 0 .2 3 7 3 -1 .4 9 5 0 0 .0 3 9 8 -2 .2 9 1 9 0 .1 7 9 7 A g e * J u n 0 2 -2 .6 5 7 5 0 .1 9 1 1 -1 .1 6 0 0 0 .0 3 7 1 -1 .7 4 6 5 0 .1 4 6 5 A g e * J u l 0 2 -2 .1 4 4 6 0 .1 5 9 4 -1 .0 9 1 1 0 .0 3 4 7 -1 .6 1 5 1 0 .1 2 3 4 A g e * A u g 0 2 -1 .9 6 8 8 0 .1 3 5 1 -1 .1 2 8 8 0 .0 3 2 6 -1 .5 4 0 9 0 .1 0 5 7 A g e * S e p 0 2 -1 .6 4 3 3 0 .1 1 6 6 -1 .0 7 9 5 0 .0 3 0 8 -1 .4 4 7 8 0 .0 9 2 0 A g e * O c t 0 2 -1 .5 5 6 9 0 .1 0 2 5 -0 .9 0 4 8 0 .0 2 9 2 -1 .6 4 1 8 0 .0 8 1 4 A g e * N ov 0 2 -1 .5 0 7 9 0 .0 9 0 4 -0 .8 4 2 9 0 .0 2 7 7 -1 .4 1 1 8 0 .0 7 2 4 A g e * D e c 0 2 -1 .2 2 1 0 0 .0 8 0 5 -0 .6 6 2 3 0 .0 2 6 4 -1 .1 3 2 3 0 .0 6 5 0
N o t a ll c o n s o le s p e c i…c m o nth e ¤e c t s re p o rte d . A ll m o d e ls in c lu d e v id e o g a m e F E a n d a g e re g re s s o r
Table 13: Competitive Software Test 3
P ric e (t) -P ric e (t-1 ) G a m e C u b e P lay S t a t io n 2 X b ox C o e ¤ S td E rr. C o e ¤ S td E rr. C o e ¤ S td E rr. J a n 0 2 1 8 .2 7 4 3 1 .6 5 3 8 6 .3 0 7 8 0 .6 9 7 4 1 2 .9 5 3 4 1 .3 0 2 0 Fe b 0 2 1 8 .3 9 8 0 1 .4 1 2 4 7 .0 9 7 3 0 .6 7 5 3 1 0 .7 6 4 6 1 .1 8 0 9 M a r 0 2 5 .9 0 1 4 3 1 .3 5 4 4 2 .1 6 3 7 0 .6 7 0 1 4 .5 2 9 4 8 1 .1 3 2 9 A p r 0 2 4 .8 2 0 6 5 1 .3 1 6 3 3 .4 9 0 1 0 .6 6 2 1 3 .3 8 0 6 7 1 .0 9 1 3 M ay 0 2 1 2 .3 7 8 9 1 .2 2 9 9 8 .2 3 4 0 0 .6 4 4 9 7 .3 6 1 3 1 1 .0 4 9 1 J u n 0 2 7 .0 9 3 6 5 1 .2 0 1 7 3 .6 6 8 6 0 .6 4 2 3 5 .7 5 9 7 2 1 .0 1 7 4 J u l 0 2 1 0 .2 7 8 5 1 .1 2 9 8 4 .0 7 0 0 0 .6 3 3 8 8 .1 2 4 6 5 0 .9 5 4 8 A u g 0 2 1 5 .9 8 7 5 0 .9 9 7 8 7 .5 6 1 5 0 .6 0 9 5 9 .7 9 9 9 5 0 .8 7 4 2 S e p 0 2 1 3 .1 1 7 8 0 .9 0 2 9 6 .5 7 9 5 0 .5 9 4 6 6 .4 4 1 7 7 0 .8 1 7 4 O c t 0 2 1 3 .6 2 0 5 0 .8 1 2 1 6 .7 2 1 2 0 .5 7 4 8 9 .7 8 9 2 2 0 .7 5 3 7 N ov 0 2 6 .7 5 4 8 7 0 .7 8 3 7 4 .8 3 0 3 0 .5 7 2 6 4 .6 0 6 5 0 0 .7 3 7 6 D e c 0 2 2 .5 2 0 6 6 0 .7 7 5 5 3 .3 7 8 5 0 .5 6 9 3 2 .1 0 1 2 0 0 .7 3 2 2