Peer effects and endogenous social interactions

TL;DR

This paper presents a new method for addressing peer selection bias in social interaction models by using inherent restrictions to construct instrumental variables, enabling a straightforward two-stage least squares estimation without modeling peer choice mechanisms.

Contribution

It introduces an innovative instrumental variable approach based on inherent restrictions, avoiding the need to specify peer selection processes in linear-in-means models.

Findings

Provides a practical two-stage least squares estimator for endogenous social interactions.

Circumvents the need for explicit modeling of peer selection mechanisms.

Enhances the robustness of social interaction analysis.

Abstract

We introduce an approach to deal with self-selection of peers in the linear-in-means model. Contrary to the existing proposals we do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent to many such specifications to construct intuitive instrumental variables. These restrictions are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the link behavior between other pairs of individuals. A two-stage least-squares estimator of the linear-in-means model is then readily obtained.