This paper presents a spatial econometric method for characterizing productivity comovement across sectors of the U.S. economy. Input‐output relations provide an economic distance measure that is used to characterize interactions between sectors, as well as conduct estimation and inference. We construct two different economic distance measures. One metric implies that two sectors are close to one another if they use inputs of other industrial sectors in nearly the same proportion, and the other metric implies that sectors are close if their outputs are used by the same sectors. Our model holds that covariance in productivity growth across sectors is a function of economic distance. We find that (1) positive cross‐sector covariance of productivity growth generates a substantial fraction of the variance in aggregate productivity, (2) cross‐sector productivity covariance tends to be greatest between sectors with similar input relations, and (3) there are constant to modest increasing returns to scale. We test and reject the hypothesis that these correlations are due to a common shock.

We develop and estimate a model of student study time choices on a social network. The model is designed to exploit unique data in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time in a heterogeneous manner. Social network data allow us to embed study time and resulting academic achievement in an estimable equilibrium framework. We develop a specification test that exploits the equilibrium nature of social interactions and use it to show that novel study propensity measures mitigate econometric endogeneity concerns.