Using randomization to identify social influence in mobile networks

Identification of social influence in observational data is a difficult task. Endogeneity issues such as homophily, correlated unobservables and simultaneity raise challenges to the researchers interested in establishing causality and in consistently measuring its magnitude. In this paper we apply randomization techniques to identify social influence in a mobile network setting. Randomization methods consist in generating pseudo-samples of the original data by selectively permuting the values of some variables among observations, and estimating empirical distributions of a parameter of interest under the null hypothesis that such permutations are random. We show that randomization methods are a viable strategy to identify social influence in contexts where all adoption is observed and the date of adoption is available. Furthermore, we show that these methods provide a lower bound for the magnitude of the effect of peer influence. We use a comprehensive panel of data from a large European mobile carrier in one country. The data comprise Call Detailed Records for all the subscribers in this carrier for a period of 11 months. We also have information on pricing plans, adoption of products, promotions and handsets. We estimate the effect of peer influence in six of these promotions. We provide evidence for negative peer influence in their adoption. Peer influence reduces adoption for these promotions between 3% and 9%. Peer influence helps to share information about new promotions but also signals who has already adopted them and, in many cases, such as free calls, having neighbors who adopted the promotion is enough to benefit from it.