  Quantitative empirical inquiry in international relations often relies on
dyadic data. Standard analytic techniques do not account for the fact that
dyads are not generally independent of one another. That is, when dyads share a
constituent member (e.g., a common country), they may be statistically
dependent, or "clustered." Recent work has developed dyadic clustering robust
standard errors (DCRSEs) that account for this dependence. Using these DCRSEs,
we reanalyzed all empirical articles published in International Organization
between January 2014 and January 2020 that feature dyadic data. We find that
published standard errors for key explanatory variables are, on average,
approximately half as large as DCRSEs, suggesting that dyadic clustering is
leading researchers to severely underestimate uncertainty. However, most (67%
of) statistically significant findings remain statistically significant when
using DCRSEs. We conclude that accounting for dyadic clustering is both
important and feasible, and offer software in R and Stata to facilitate use of
DCRSEs in future research.
