Count Data Models with Heterogeneous Peer Effects under Rational Expectations

2024-05-27

AI Summary

Identifying heterogeneous peer effects in count-response data remains challenging due to unobserved heterogeneity and network endogeneity. Method: This paper develops a rational-expectations game-theoretic model under incomplete information to characterize individual decision-making under heterogeneous peer influence in social networks. It extends linear identification conditions to generalized nonlinear count models for the first time; proposes a novel network-exogeneity identification strategy leveraging “friends-of-friends” to jointly identify network endogeneity and heterogeneous peer effects; and introduces a nested pseudo-likelihood (NPL) estimator, implemented in the open-source R package CDatanet for empirical replication. Contribution/Results: Empirical analysis reveals significant same-gender peer effects on girls’ extracurricular participation, whereas boys’ participation is unaffected by same-gender peers—highlighting gender-differentiated social influence mechanisms in adolescent behavior.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Social Choice / Voting
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

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 packag--named CDatanet--is available for implementing the model.

Problem

• Models count data with heterogeneous peer effects
• Identifies peer influence using network structure conditions
• Estimates gender differences in extracurricular activity responses

Innovation

• Count data model with heterogeneous peer effects
• Identification via friends of non-direct friends
• Estimation using nested pseudo-likelihood approach