  This paper is concerned with inference in the linear model with dyadic data.
Dyadic data is data that is indexed by pairs of "units", for example trade data
between pairs of countries. Because of the potential for observations with a
unit in common to be correlated, standard inference procedures may not perform
as expected. We establish a range of conditions under which a t-statistic with
the dyadic-robust variance estimator of Fafchamps and Gubert (2007) is
asymptotically normal. Using our theoretical results as a guide, we perform a
simulation exercise to study the validity of the normal approximation, as well
as the performance of a novel finite-sample correction. We conclude with
guidelines for applied researchers wishing to use the dyadic-robust estimator
for inference.
