  Social interactions determine many economic behaviors, but information on
social ties does not exist in most publicly available and widely used datasets.
We present results on the identification of social networks from observational
panel data that contains no information on social ties between agents. In the
context of a canonical social interactions model, we provide sufficient
conditions under which the social interactions matrix, endogenous and exogenous
social effect parameters are all globally identified. While this result is
relevant across different estimation strategies, we then describe how
high-dimensional estimation techniques can be used to estimate the interactions
model based on the Adaptive Elastic Net GMM method. We employ the method to
study tax competition across US states. We find the identified social
interactions matrix implies tax competition differs markedly from the common
assumption of competition between geographically neighboring states, providing
further insights for the long-standing debate on the relative roles of factor
mobility and yardstick competition in driving tax setting behavior across
states. Most broadly, our identification and application show the analysis of
social interactions can be extended to economic realms where no network data
exists.
