Inference in Linear Dyadic Data Models with Network Spillovers

TL;DR

This paper highlights the limitations of traditional dyadic-robust variance estimators in networked data, proposing a new consistent estimator that accounts for network spillovers, with demonstrated effectiveness through simulations and an application to European Parliament voting.

Contribution

It introduces a novel variance estimator for dyadic data with network spillovers, improving inference accuracy in such contexts.

Findings

Dyadic-robust estimators can be biased with network spillovers.

The proposed estimator performs well in finite samples.

Application to European Parliament data illustrates practical relevance.

Abstract

When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using ``dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because indirect links may exist due to network connections, generating correlated outcomes. Hence, ``dyadic-robust'' estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians' voting behavior when they are seating neighbors in the European Parliament.