Professor Bryan S. Graham

We study the effects of counterfactual teacher-to-classroom assignments on average student achievement in elementary and middle schools in the US.

Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another.

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables defined for all n def = (N/2) unordered pairs of agents/nodes in a weighted network of order N).

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables.

An econometric model of network formation with degree heterogeneity

We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data.

In social and economic networks linked agents often share additional links in common. There are two competing explanations for this phenomenon. First, agents may have a structural taste for transitive links – the returns to linking may be higher if two agents share links in common. Second, agents may assortatively match on unobserved attributes, a process called homophily. I study parameter identifiability in a simple model of dynamic network formation with both effects. Agents form, maintain, and sever links over time in order to maximize utility.

Consider two heterogenous populations of agents who, when matched, jointly produce an output, Y. For example, teachers and classrooms of students together produce achievement, parents raise children, whose life outcomes vary in adulthood, assembly plant managers and workers produce a certain number of cars per month, and lieutenants and their platoons vary in unit effectiveness. Let W ∈ 𝕨= {ω1, . . . ,ωJ} and X ∈ 𝕩 = {x1, . . . ,xK} denote agent types in the two populations. Consider the following matching mechanism: take a random draw from the W = wj subgroup of the first population and match her with an independent random draw from the X = xk subgroup of the second population. Let β ;(wj, xk), the average match function (AMF), denote the expected output associated with this match. We show that (i) the AMF is identified when matching is conditionally exogenous, (ii) conditionally exogenous matching is compatible with a pairwise stable aggregate matching equilibrium under specific informational assumptions, and (iii) we calculate the AMF’s semiparametric efficiency bound.

This paper formulates a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent level heterogeneity in link surplus (degree heterogeneity).

The authors propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data.