  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.
