Measurement Error and Peer Effects in Networks

2026-07-31

AI Summary

This study addresses the bias and potential spurious significance in peer effect estimation arising from measurement error in linear-in-means models of social networks. It demonstrates that the direction of this bias hinges on the interaction between individual attributes and network structure. Departing from conventional approaches that rely on external instrumental variables, the paper innovatively leverages intrinsic network topology for identification, thereby circumventing traditional paradigms for handling measurement error. Building upon generalized method of moments (GMM) and two-stage least squares (2SLS), the authors develop a class of consistent estimators that explicitly incorporate network structure. The validity and robustness of these estimators are confirmed through extensive Monte Carlo simulations.

Abstract

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need for additional external information. Based on these identification results, we present consistent GMM and 2SLS estimators that are easily implementable. Our results are illustrated by means of a Monte Carlo simulation.

Problem

• measurement error
• peer effects
• networks
• linear-in-means model
• attenuation bias

Innovation

• measurement error
• peer effects
• network structure
• identification
• GMM