Count Data Models with Heterogeneous Peer Effects under Rational Expectations

TL;DR

This paper introduces a new count data peer effect model under rational expectations that captures heterogeneity across groups, with an estimation method and an empirical application showing gender differences in peer responsiveness.

Contribution

It develops a novel identification strategy for nonlinear count data models with peer effects and provides an accessible R package for implementation.

Findings

Females are more responsive to peers than males.

The model successfully captures heterogeneity in peer effects.

The R package CDatanet facilitates practical application.

Abstract

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.