Accepted and Published papers

Working Papers

  • Estimating Peer Effects using Partial Network Data, September 2023 - with Vincent Boucher (R&R at 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, misclassified links, and aggregated relational data. 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
  • Healthcare Quality by Specialists under a Mixed Compensation System: an Empirical Analysis, August 2023 - whith Damien Echevin and Bernard Fortin (R&R at Health Economics)

    We analyze the effects of a mixed compensation (MC) scheme for specialists on the quality of their healthcare services. To do so, we exploit a major reform that was implemented in Quebec (Canada) in 1999. The government introduced a payment mechanism combining a fixed per diem with a reduced fee per clinical service. Using panel patient-doctor data covering the period 1996-2016 and including 320,441 patients, we estimate a multi-state multi-spell hazard model with correlated heterogeneity, analogous to a difference-in-differences approach. We compute three output-based quality indicators from our model. Our results suggest that the reform reduced the quality of MC specialist services as measured by the risk of re-hospitalization within 30 days after discharge and the risk of mortality within one year after discharge. These effects vary depending upon the specialty of the treating doctor.

  • 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
  • The Role of Child Gender in the Formation of Parents' Social Networks, February 2024 - with Asad Islam, Michael Vlassopoulos, and Yves Zenou

    Social networks play an important role in various aspects of life. While extensive research has explored factors like gender, race, and education in network formation, one dimension that has received less attention is the gender of one's child. Children tend to form friendships with same-gender peers, potentially leading their parents to interact based on their child's gender. Focusing on households with children aged 3-5, we leverage a rich dataset from rural Bangladesh to investigate the role of children's gender in parental network formation. We estimate an equilibrium model of network formation that considers a child's gender alongside other socioeconomic factors. Counterfactual analyses reveal that children's gender significantly shapes parents' network structure. Specifically, if all children share the same gender, households would have approximately 15% more links, with a stronger effect for families having girls. Importantly, the impact of children's gender on network structure is on par with or even surpasses that of factors such as income distribution, parental occupation, education, and age. These findings carry implications for debates surrounding coed versus single-sex schools, as well as policies that foster inter-gender social interactions among children.

  • 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
  • Count Data Models with Heterogeneous Peer Effects under Rational Expectations, May 2024

    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

Work In Progress

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

  • Physicians' Financial Incentives - whith Damien Echevin

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