Estimating Peer Effects Using Partial Network Data
Bibliographic Data
| ID | 19317832 |
|---|---|
| Authors | Vincent Boucher (0000-0003-2299-4168, Department of Economics, Université Laval, CRREP, CREATE, CIRANO; [email protected]), Aristide Houndetoungan (0009-0003-2733-9362, Department of Economics, Université Laval [email protected]) |
| Year | 2025 |
| Pages | 1-48 |
| Publication date | 2025-10-27 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The Review of Economics and Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0034-6535 • E-ISSN: 1530-9142 |
| Publisher | MIT Press (PUBLISHER • US) |
| DOI | 10.1162/rest.a.1630 |
| OpenAlex | W4407608984 |
| Language | EN |
| Citations received | 4 |
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
Advanced Causal Inference Techniques · Computer Science · Intergenerational and Educational Inequality Studies · School Choice and Performance
Survey-calibrated agent-based modeling of peer targeting for promoting physical activity among adolescents
Count Data Models With Heterogeneous Peer Effects Under Rational Expectations
Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students
Random Assignment with Nonrandom Peers
| Unique citing works | 4 |
|---|---|
| Citations per year | 2 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |