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.

This paper presents an empirical analysis of the strategic forces shaping U.S.-Mexico trade relationships and the possibilities of extending the trade agreement to the rest of the Americas. The paper concludes that constituency interests, party loyalty, the proportion of a state's population of Hispanic origin, and the influence of textile-related employment in the state were significant explanatory factors in the Congressional Fast Track vote that occurred in May of 1991.