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Research

Accepted and Published papers


Working Papers

  • Identification of Heterogeneous Peer Effects, July 2026 — with Eyo I. Herstad and Myungkou Shin

    We develop a model of peer effects where each peer has a separate effect depending on their rank in the distribution of peers' outcomes. Our model admits a unique equilibrium, and model parameters can be identified using peers' exogenous characteristics. To obtain a more parsimonious model of peer effects, we introduce a tractable specification based on quantile-dependent peer effect coefficients, and develop a specification test. Applying the model to several student outcomes in the Add Health data, we uncover heterogeneous and often non-monotonic spillovers that cannot be captured by existing models. Our results have direct implications for counterfactual analysis, suggesting that a student's influence in a network depends not only on network structure, but also on that student's position in the outcome distribution of their peers.

    [Draft] [Replication] [R Package] [Vignette]

  • Inference for Two-Stage Extremum Estimators, February 2024 — with Abdoul Haki Maoude (major revision in progress)

    We present a simulation-based approach to approximate the asymptotic variance and asymptotic distribution function of two-stage estimators. We focus on extremum estimators in the second stage and consider a large class of estimators in the first stage. This class includes extremum estimators, high-dimensional estimators, and other types of estimators (e.g., Bayesian estimators). We accommodate scenarios where the asymptotic distributions of both the first- and second-stage estimators are non-normal. We also allow for the second-stage estimator to exhibit a significant bias due to the first-stage sampling error. We introduce a debiased plug-in estimator and establish its limiting distribution. Our method is readily implementable with complex models. Unlike resampling methods, we eliminate the need for multiple computations of the plug-in estimator. Monte Carlo simulations confirm the effectiveness of our approach in finite samples. We present an empirical application with peer effects on adolescent fast-food consumption habits, where we employ the proposed method to address the issue of biased instrumental variable estimates resulting from the presence of many weak instruments.

    [arxiv] [Replication]

Work In Progress

  • Friendship Networks and Social Diversity at School: Evidence from a Desegregation Program — with Ghazala Azmat, Yann Bramoullé, Julien Grenet, Élise Huillery, and Youssef Souidi (draft coming soon)

    • We analyze the impact of a national large-scale desegregation program, targeting a greater mixing of students from different social backgrounds in middle schools, on friendship networks. We compare students in sites covered by the desegregation program (“treatment” group) with students in “matched” sites that are not covered (“control” group). We first document significant homophily with respect to socio-economic status in control schools. We then assess the effect of the program on friendship networks, finding that status homophily is higher in treated schools, which have more diverse student populations. Both baseline homophily and the increase in homophily due to the treatment reduce the effectiveness of the program in fostering more diverse friendships. We propose a novel decomposition of the treatment effect into a composition and a homophily effect, and we develop a new methodology to account for censoring in the econometrics of network formation.

  • Asymmetries in Peer Effects for Continuous Outcomes — with Mathieu Lambotte (draft coming soon)

    • Substantial evidence shows that individuals tend to conform to their peers because deviating from peer behavior incurs a social penalty. We develop a structural model of peer effects with asymmetric conformity. Social penalties depend on whether individuals perform below or above each of their friends, generating distinct influences from high- and low-performing peers. The resulting game generalizes the standard conformity model but features non-differentiable best-response functions. Under reasonable conditions, we establish the existence and uniqueness of equilibrium and show that the model is identified. We propose a GMM estimator that identifies both types of peer effects using friends' characteristics and exogenous predictions of their status as low- or high-performing peers as instruments. We illustrate the method using Add Health data and uncover diverse patterns of conformity across multiple outcomes. We then study the design of targeted interventions under limited resources. We find that ignoring asymmetries in peer effects leads to inefficient treatment assignment and substantial welfare losses, which may exceed the welfare losses that arise in the absence of social interactions.

  • The Impact of Public Policies on the Dynamics of School Enrollment and Dropout: Evidence from Canada — Bernard Fortin, Catherine Michaud-Leclerc, and Safa Ragued

  • Partially Identifying Peer Effects Models with Mismeasured Network Data — with Ismael Mourifié

  • Overlapping Ownership and Competition in Central Bank Auctions — with Mathieu Marcoux

  • Peer Effects in Active Labor Market Policies — with Jérémy Hervelin

  • Quasi-Maximum Likelihood Estimator for Peer Effect Models with Partial Network Data