Vincent Boucher, Aristide Houndetoungan
arXiv 9 Sep 2025 · Econometrics · publishedThe Review of Economics and Statistics (2025) · 13 citations (OpenAlex)
arXiv:2509.08145 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Graham, B. S (2017) An econometric model of network formation with degree heterogeneity | 0.585 | 3 | 1 | 100% |
| 2 | Boucher, V. and I. Mourifié (2017) My friend far, far away: a random field approach to exponential random graph models self | 0.405 | 1 | 1 | 100% |
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