Cluster-Robust Variance Estimation for Dyadic Data

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a nonparametric sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and applications to international relations and speed dating

Environmental Clean-Up and Property Price Change

In my experiment, I am trying to find the value, to Lorain County property owners, of cleaning up the Black River which runs through Lorain County. This study involves a number of property variables, a number of neighborhood variables, and the environmental variables. The hedonic price function takes this general form:Property Price = c + β1Property Characteristics + β2Neighborhood Characteristics + β3Environmentai Characteristics + u. This equation says that the price of a piece of property is a function of several things: the characteristics of the property, the characteristics of the neighborhood it\u27s in, and the characteristics of the environment. The slope coefficients β1, β2, and β3 are the hedonic prices of the property, neighborhood, and environmental characteristics, respectively. u represents the combined effect of all the housing characteristics about which I have no information

Environmental Clean-Up and Property Price Change

In my experiment, I am trying to find the value, to Lorain County property owners, of cleaning up the Black River which runs through Lorain County. This study involves a number of property variables, a number of neighborhood variables, and the environmental variables. The hedonic price function takes this general form:Property Price = c + β1Property Characteristics + β2Neighborhood Characteristics + β3Environmentai Characteristics + u. This equation says that the price of a piece of property is a function of several things: the characteristics of the property, the characteristics of the neighborhood it\u27s in, and the characteristics of the environment. The slope coefficients β1, β2, and β3 are the hedonic prices of the property, neighborhood, and environmental characteristics, respectively. u represents the combined effect of all the housing characteristics about which I have no information