Department of Economics Spring 2021 Brown Bag Series

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Date/Time
Date(s) - 02/15/2021
12:00 pm - 1:00 pm

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The Brown Bag Seminar Series offers current ECON graduate students the opportunity to present ongoing research or to workshop new ideas. The seminars are held on Mondays from 12:00-1:00pm and will be held over Zoom for the fall semester.

For more information on the series or specific seminars, please contact Elene Murvanidze at Elene.Murvanidze@colostate.edu.

Ph.D. graduate student Sulagna Dasgupta from the University of Chicago will be presenting at the first Brown Bag of the spring semester – Monday, February 15th from 12:00-1:00 pm.

Join Zoom Meeting
https://zoom.us/j/97336436648

Meeting ID: 973 3643 6648

She will be presenting her paper: “Optimal Information Structures in Matching Markets.”

Abstract: In the school-choice problem, students are allocated to schools based on priority orderings of schools and their own preferences. However, students often cannot determine, a priori, how effective each school would be for them, because they do not have enough information about the schools, their own fit with each school, etc. In this context, I try to answer the question: How should a benevolent principal optimally reveal information to the agents (students) to maximize welfare, in an environment where agents have no private information to start with? As a benchmark, I first show that when using any of the standard strategyproof ordinal mechanisms, such as Student Proposing Deferred Acceptance, Serial Dictatorship, Random Priority or Top Trading Cycle, letting each agent know his true ordinal ranking is almost never a social¬†welfare-maximizing information policy. By way of a partial solution, I then propose a simple signal I call the Object Recommendation (OR) Signal. Under i.i.d. agent priors satisfying a mild reasonableness criterion, I show that, when agents’ a priori relative¬†preferences over the objects are “not too strong”, the OR Signal, used together with any of the aforementioned standard mechanisms, not only maximizes welfare, but achieves first-best, i.e. the unconstrained maximum total ex-ante welfare.