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"Estimating Peer Effects Using Partial Network Data"

Abstract :
We study the estimation of peer effects through social networks when researchers do not observe the network structure. Instead, we assume that researchers know (have a consistent estimate of) the distribution of the network. We show that this assumption is sufficient for the estimation of peer effects using a linear-in-means model. We present and discuss important examples where our methodology can be applied. In particular, we provide an empirical application to the study of peer effects on students’ academic achievement.

mars 2022 :

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