Estimating Causal Installed-Base Effects: A Bias-Correction Approach
研究了在消费者需求模型中估计安装基数（过去采用者行为）的因果效应时，固定效应和随机效应估计量存在不一致性，提出了工具变量和偏差校正两种新方法，并在丰田普锐斯混合动力车采用数据中验证了社会影响的存在。
New empirical models of consumer demand that incorporate social effects seek to measure the causal effect of past adopter's behavior—the “installed-base”—on current adoption behavior. Identifying such causal effects is challenging due to several alternative confounds that generate correlation in agents' actions. In the absence of experimental variation, a preferred solution has been to control for these spurious correlations using a rich specification of fixed effects. The authors show that fixed-effects estimators of this sort are inconsistent in the presence of installed-base effects; in simulations, random-effects specifications perform even worse. The analysis reveals the tension the applied empiricist faces in this area: a rich control for unobservables increases the credibility of the reported causal effects, but the incorporation of these controls introduces biases of a new kind in this class of models. The authors present two solutions: a modified version of an instrumental variable approach and a new bias-correction approach, both of which deliver consistent estimates of causal installed-base effects. The empirical application to the adoption of the Toyota Prius Hybrid in California shows evidence for social influence in diffusion and reveals that implementing the bias correction reverses the sign of the measured installed-base effect. The authors also discuss implications of the results for identification of models in marketing involving state dependence in demand, and incorporating discrete games of strategic interaction.