Research
Accepted and Published papers
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Echevin, D., Fortin, B., Houndetoungan, A. (2025). Healthcare Quality by Specialists under a Mixed Compensation System: an Empirical Analysis. Health Economics (Accepted).
Working Paper Dufays, A., Houndetoungan, A., & Coën, A. (2022). Selective linear segmentation for detecting relevant parameter changes. Journal of Financial Econometrics, 20(4), 762-805.
Published Version Working Paper + Online Appendix
Working Papers
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Estimating Peer Effects using Partial Network Data, March 2025 - with Vincent Boucher (R&R at the Review of Economics and Statistics)
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 first-order downward bias on estimated peer effects.
Paper Online Appendix R Package Vignette -
Count Data Models with Heterogeneous Peer Effects under Rational Expectations, May 2024 (R&R at the Journal of Applied Econometrics)
This paper develops a micro-founded peer effect model for count responses using a game of incomplete information. The model incorporates heterogeneity in peer effects through agents' groups based on observed characteristics. Parameter identification is established using the identification condition of linear models, which relies on the presence of friends' friends who are not direct friends in the network. I show that this condition extends to a large class of nonlinear models. The model parameters are estimated using the nested pseudo-likelihood approach, controlling for network endogeneity. I present an empirical application on students' participation in extracurricular activities. I find that females are more responsive to their peers than males, whereas male peers do not influence male students. An easy-to-use R package—named CDatanet—is available for implementing the model.
Paper R Package -
Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students, May 2024 - with Cristelle Kouame and Michael Vlassopoulos (Reject and Resubmit at the Journal of Applied Econometrics)
Peer influence on effort devoted to some activity is often studied using proxy variables when actual effort is unobserved. For instance, in education, academic effort is often proxied by GPA. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40\% lower than those obtained using our approach.
Paper Online Appendix Replication Code Inference for Two-Stage Extremum Estimators, February 2024 - with Abdoul Haki Maoude
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.
Paper Online Appendix Replication Code
Work In Progress
Quantile Peer Effect Models (draft available soon)
Friendship Networks and Social Diversity at School: Evidence from a Desegregation Program - with Ghazala Azmat, Yann Bramoullé, Julien Grenet, Élise Huillery, and Youssef Souidi
Quasi-Maximum Likelihood Estimator for Peer Effect Models with Partial Network Data
Physicians' Financial Incentives - with Damien Echevin
Peer Effects in Active Labor Market Policies - with Jérémy Hervelin
I propose a flexible structural model to estimate peer effects at multiple quantiles of peer outcomes. This approach allows peers with low, middle, and high outcomes to exert distinct effects. It accommodates more flexible peer influence patterns than models that rely on aggregate measures, such as the average, minimum, maximum, or functional forms imposing constant elasticity of substitution. I show that the model has a unique equilibrium despite the nonsmooth nature of the quantile function. I analyze identification and demonstrate that the structural parameters can be estimated using a straightforward instrumental variable approach. Applying the model to various outcomes studied in the literature, I find that peer effect dynamics are intricate and rarely uniform, challenging standard assumptions. This finding has important policy implications: key player status in a network depends not only on the network structure but also on the distribution of outcomes within the population.
We analyze the impact of a large-scale desegregation program on friendship networks in middle schools. 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. We find 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.