Technological tying and the intensity of price competition: An empirical analysis of the video game industry

Abstract

Using data from the 128-bit video game industry I evaluate the impact technologically tying has on the intensity of console price competition and the incentives for hardware firms to tie their produced software to their hardware. Tying occurs when a console hardware manufacturer produces software that is incompatible with rival hardware. There are two important trade-offs an integrated firm faces when implementing a technological tie. The first is an effect that increases console market power and forces hardware prices higher. The second, an effect due to the integration of the firm, drives prices lower. A counterfactual exercise determines technological tying of hardware and software increases console price competition; console makers subsidize consumer hardware purchases in order to increase video games sales, in particular their tied games, where the greatest proportion of industry profits are made. I also determine technological tying to be a dominant strategy for hardware manufacturers when software development costs are low.

Appendices

Appendix A: Computational details

Here I briefly describe how I update $\lambda _{i,t}^{h}$. It involves determining market shares in the first period given $\lambda _{i,t=0}^{h}$ and then computing the distribution of consumers who remain in the market according to rule

$$\lambda_{i,t+1}^{h}=\frac{M_{t=0}^{h}\lambda_{i,t=0}^{h}\prod_{t=0}^{t}(1-s_{i,t})}{\sum_{i=1}^{I}\left(M_{t=0}^{h}\lambda_{i,t=0}^{h}\prod_{t=1}^{t}(1-s_{i,t})\right)} $$

where s _i,t is the probability a consumer buys a piece of hardware in period t. For completeness, to recover the expected value function I implement a standard value function iteration procedure with a linear interpolation between the 40 discrete grid points to calculate the expected value function in period t. This procedure is run in steps 2 and 4 of the estimation procedure above.

Appendix B: Video game competition

In the model above, one of the main assumptions I implement is in regard to software competition. I make the assumption that video games do compete with one another rather than assume games are monopolists as the previous works of Nair (2007) and Lee (2013) do. In order to validate this assumption I present the results of two tests below. The first determines whether cross-price effects are present while the second tests whether falling prices are a consequence of competitive conditions. In determining whether there are cross-price effects among software titles I implement a nested logit model for software demand. Under such model, however, there are several concerns. One is that cross-price substitution might be underestimated if game developers strategically release video games as to minimize the cannibalization of similar games currently in the market. I follow a similar specification to that of Nair (2007) that tries to account for this endogeneity with a nested logit model with nests corresponding to the video game genre. I also include a covariate that captures game age. The video game demand specification is

$$ln(s_{k_{j}t}/s_{0_{j}t})=\alpha_{j}+\lambda(t-r_{k_{j}})+\beta p_{k_{j}t}+\sigma ln(s_{k_{j}t|g})+\eta ln\left(Num_{t}^{SW}\right)+\psi_{k_{j}t} $$

where t indexes month, $r_{k_{j}}$ is the release date of game k _j , $p_{k_{j}t}$ is the price, $s_{k_{j}t}$ is the market share, $s_{0_{j}t}$ is the outside good’s share, $s_{k_{j}t|g}$ is the within-genre share of game k _j in period t, and l n(N u m ^SW) is the log of the total number of available games on platform j. Moreover, the parameter σ captures the degree of correlation of utilities among games in a given genre. A small σ near zero infers little correlation among genre games while a larger value indicates larger cross-price effects. Thus, a test of competition among software titles would be to determine if σ is statistically different from zero. Nonetheless, to properly test that we need to account for the endogeneity of price, release timing, 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 fixed effects. “With the inclusion of such all variation in demand arising from aspects of game-quality is controlled for,” writes 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 specifications with quadratic and cubic software age covariates. From the results it is evident that video games compete against one another and are not monopolists.

If the results from the first test in Table 12 are not conclusive enough I present a second test to illustrate that software video game prices largely decline because of increased video game competition. For this test I pool all game data across each console and regress software price on age, game fixed effects, and the interaction of age and console-specific month fixed effects. I hence measure the rate at which prices fall after controlling for game quality via game fixed effects. Negative and statistically significant estimates of the interaction terms therefore indicate that prices fall because of the competitive interaction of software titles (Table 13).

Table 12 Competitive software tests

Table 13 Competitive software tests