Joint work with Santiago Pereda Fernández. In preparation for submission.
Abstract. The estimation of spillover and peer effects presents challenges that are still unsolved. In fact, even if separate algebraic identification of the endogenous and exogenous effects is possible, these might be contaminated by the simultaneous dependence of outcomes, covariates and the network structure upon spatially correlated unobservables. In this paper we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model in presence of linear forms of endogeneity. We show that identification is possible if the network of social interactions is non-overlapping up to three degrees of separation, and the spatial matrix that characterizes the co-dependence of individual covariates and peers’ unobservables is known to the econometrician. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations.