TszKin Julian Chan, Juan Estrada, Kim Huynh, David Jacho-Chavez, Chungsang Tom Lam, Leonardo Sanchez-Aragon
arXiv 2 Aug 2026 · Econometrics
arXiv:2608.01405 · PDF · Extracted main text
This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of $ψ$-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator's strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers.
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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 | Liu, Xiaodong, Eleonora Patacchini, and Yves Zenou (2014) Endogenous Peer Effects: Local Aggregate or Local Average? | 0.928 | 4 | 3 | 100% |
| 2 | Hasan, Sharique and Rembrand Koning (2019) Prior Ties and the Limits of Peer Effects on Startup Team Performance | 0.874 | 5 | 2 | 100% |
| 3 | Kojevnikov, Denis, Vadim Marmer, and Kyungchul Song (2021) Limit Theorems for Network Dependent Random Variables | 0.855 | 16 | 4 | 62% |
| 4 | Carrell, Scott E., Bruce I. Sacerdote, and James E. West (2013) From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation | 0.811 | 4 | 2 | 100% |
| 5 | Bramoullé, Yann, Habiba Djebbari, and Bernard Fortin (2009) Identification of Peer Effects through Social Networks | 0.737 | 3 | 2 | 100% |
| 6 | Johnsson, Ida and Hyungsik Roger Moon (2019) Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach | 0.737 | 3 | 2 | 100% |
| 7 | Sacerdote, Bruce (2001) Peer Effects with Random Assignment: Results for Dartmouth Roommates | 0.737 | 3 | 2 | 100% |
| 8 | Erdös, P and A Rényi (1959) On Random Graphs | 0.644 | 4 | 1 | 100% |
| 9 | Athey, Susan and Guido W Imbens (2017) The Econometrics of Randomized Experiments | 0.644 | 2 | 2 | 100% |
| 10 | Auerbach, Eric (2022) Identification and Estimation of a Partially Linear Regression Model Using Network Data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Regression with Observational Multilayered Network Data | 1.000 | 8 | 4 |
| 2 | Estimating Peer Influence in Multilayer Networks | 0.405 | 1 | 1 |