Estimating Peer Effects Using Partial Network Data

Fuente: arXiv
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Main Authors: Boucher, Vincent, Houndetoungan, Aristide
Format: Preprint
Published: 2025
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author Boucher, Vincent
Houndetoungan, Aristide
author_facet Boucher, Vincent
Houndetoungan, Aristide
contents We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students' academic achievement using the widely used Add Health database, and show that network data errors have a large downward bias on estimated peer effects.
format Preprint
id arxiv_https___arxiv_org_abs_2509_08145
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Estimating Peer Effects Using Partial Network Data
Boucher, Vincent
Houndetoungan, Aristide
Econometrics
We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students' academic achievement using the widely used Add Health database, and show that network data errors have a large downward bias on estimated peer effects.
title Estimating Peer Effects Using Partial Network Data
topic Econometrics
url https://arxiv.org/abs/2509.08145