arXiv subjects

Aristide Houndetoungan

Publications and source records attributed to Aristide Houndetoungan.

10 recordsLinked to original sources

arXiv2026

Asymmetries in Peer Effects

Individuals are often influenced by their peers because deviating from prevailing behavior entails social costs. However, existing peer effects models typically assume that individuals respond similarly to peers who perform better or worse than they do. This paper introduces a novel structural model of asymmetric peer effects in which conformity incentives depend on whether individuals perform below or above each of their peers. We establish that the model admits a unique equilibrium and show that its parameters can be identified and estimated through simple moment conditions. Applying our method to several student outcomes, we uncover strong evidence of asymmetries in peer effects. We then demonstrate that these asymmetries are highly policy-relevant by studying targeted interventions under budget constraints. Ignoring asymmetries leads to inefficient treatment allocation and substantial welfare losses, reducing welfare to levels comparable to those in a benchmark without social interactions.

econ.EM↗

arXiv2026

Identification of Heterogeneous Peer Effects

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.

econ.EM↗

arXiv2026

Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students

Peer influence on effort devoted to some activity is often studied when effort is unobserved, and the researcher instead observes an outcome that combines effort with other shocks. For instance, in education, achievement measures such as GPA reflect both effort and idiosyncratic GPA shocks. 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.

econ.EM↗

arXiv2026

Count Data Models with Heterogeneous Peer Effects under Rational Expectations

This paper develops a peer effect model for count responses under rational expectations. The model accounts for heterogeneity in peer effects across groups based on observed characteristics. Identification is based on the linear model condition that requires the presence of friends of friends who are not direct friends. I show that this identification condition extends to a broad class of nonlinear models. Parameters are estimated using a nested pseudo-likelihood approach. An empirical application to students' extracurricular participation reveals that females are more responsive to peers than males. An easy-to-use R package, CDatanet, is available for implementing the model.

econ.EM↗

arXiv2025

Estimating Peer Effects Using Partial Network Data

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.

econ.EM↗

arXiv2025

Quantile Peer Effect Models

I propose a flexible structural model to estimate peer effects across various quantiles of the peer outcome distribution. The model allows peers with low, intermediate, and high outcomes to exert distinct influences, thereby capturing more nuanced patterns of peer effects than standard approaches that are based on aggregate measures. I establish the existence and uniqueness of the Nash equilibrium and demonstrate that the model parameters can be estimated using a straightforward instrumental variable strategy. Applying the model to a range of outcomes that are commonly studied in the literature, I uncover diverse and rich patterns of peer influences that challenge assumptions inherent in standard models. These findings carry important policy implications: key player status in a network depends not only on network structure, but also on the distribution of outcomes within the population.

econ.EM↗

arXiv2024

Inference for Two-Stage Extremum Estimators

We present a simulation-based inference approach for two-stage estimators, focusing on extremum estimators in the second stage. We accommodate a broad range of first-stage estimators, including extremum estimators, high-dimensional estimators, and other types of estimators such as Bayesian estimators. The key contribution of our approach lies in its ability to estimate the asymptotic distribution of two-stage estimators, even when the distributions of both the first- and second-stage estimators are non-normal and when the second-stage estimator's bias, scaled by the square root of the sample size, does not vanish asymptotically. This enables reliable inference in situations where standard methods fail. Additionally, we propose a debiased estimator, based on the mean of the estimated distribution function, which exhibits improved finite sample properties. Unlike resampling methods, our approach avoids the need for multiple calculations of the two-stage estimator. We illustrate the effectiveness of our method in an empirical application on peer effects in adolescent fast-food consumption, where we address the issue of biased instrumental variable estimates resulting from many weak instruments.

econ.EM↗

arXiv2024

Selective linear segmentation for detecting relevant parameter changes

Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter truly vary when a change-point is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of 14 Hedge funds (HF) strategies, using an asset based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

econ.EM↗

arXiv2024

Healthcare Quality by Specialists under a Mixed Compensation System: an Empirical Analysis

We analyze the effects of a mixed compensation (MC) scheme for specialists on the quality of their healthcare services. We exploit a reform implemented in Quebec (Canada) in 1999. The government introduced a payment mechanism combining a per diem with a reduced fee per clinical service. Using a large patient/physician panel dataset, we estimate a multi-state multi-spell hazard model analogous to a difference-in-differences approach. We compute quality indicators from our model. Our results suggest that the reform reduced the quality of MC specialist services measured by the risk of re-hospitalization and mortality after discharge. These effects vary across specialties.

econ.GN↗

arXiv2024

The Role of Child Gender in the Formation of Parents' Social Networks

Social networks play an important role in various aspects of life. While extensive research has explored factors such as 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.

econ.GN↗