Choose Your Adviser Wisely: Endogenous Advisor-Advisee and Coauthorship Networks and their Implications for Early-Career Research Output, Job Market Paper
We investigate the impact of initial academic social network, formed from advisor-advisee relationships and coauthorships, for economics Ph.D. students (advisees) in the U.S. on their early stage productivity. We define the academic social network as a union of i) an advisor-advisee network and ii) a coauthorship network. We model the advisor-advisee relationships with a preferential attachment-like process based on a discrete choice model and find that advisees show weak gender homophilic preferences when choosing advisors. We further model early stage coauthorship formation of advisees through a bipartite network setup, also based on a discrete choice model, and find that advisees prefer to choose projects that are coauthored with their advisors during their graduate studies. Given the academic social network through the two networks, we find that the network statistics for advisees have significant positive correlation with early stage output but find weak evidence on the difference by gender. Through simulated synthetic data, we show that, advisee’s preference based formation results in productivity gain in percentage at the average individual level but not as much at the aggregate output level, compared to uniform random formation of the networks. This implies that the advisee’s preference based allocation of advisors to advisees is less efficient in a social planner’s view.
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The Impact of Missing Links on Linear Reduced-form Network-Based Peer Effects Estimates (with Alan Griffith), Under Review
Applied researchers frequently estimate network-based peer effects models using observed network data that includes only a subset of the true links. We consider the effects of this mismeasurement on reduced-form linear-in-means and linear-in-sums estimates. Our results require an assumption that the expected covariance of characteristics between linked agents is the same regardless of whether the link is observed or not. Analytic results show that the linear-in-means peer effects estimate is in general attenuated, and this is a special case of classical measurement error. In contrast, linear-in-sums direct and peer effect estimates may be attenuated, augmented, or consistent; the inconsistency depends upon the missingness mechanism and the relationship between the network and covariates. We demonstrate the effect of mismeasured links in both models using two datasets and through simulations. These results show that the effects of mismeasured networks on subsequent estimands is quite sensitive to the parameter that is being estimated.
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Efficient Sampling for Diffusion Processes in Networks (with Alan Griffith and Tyler McCormick)