We analyze a theoretical model in which entrepreneurs’ property rights are threatened by “raiders” who can challenge them to a contest for control of their firms. Entrepreneurs have heterogeneous productivity, and decide how much capital to invest before raiders decide whom to attack. In...

We develop a unified framework with schools and residential choices and study the welfare and distributional consequences of switching from the traditional neighborhood assignment to the celebrated Deferred Acceptance mechanism. We show that when families receive higher priorities at...

We study the problem of allocating COVID-19 vaccines or other scarce and heterogeneous medical resources to patients. We develop algorithms for maximizing aggregate match-effectiveness subject to fairness and distributional constraints. Our solution uses a novel cutoff characterization result...

We study priority-based assignment problems with distributional and diversity objectives. Our work provides an axiomatic characterization of a general class of choice rules which are based on type-specific reserves and quotas. The choice rules in the class differ by the order in which applicants...

We analyze a theoretical model in which entrepreneurs' property rights are threatened by "raiders" who can challenge them to a contest for control of their firms. Entrepreneurs have heterogeneous productivity, and decide how much capital to invest before raiders decide whom to attack. In...

In priority-based matching problems, there is a trade-off between efficiency andreducing justified-envy. We show that finding an efficient and justified-envy minimalmatching is an NP-hard problem when objects have weak priority rankings. Consequently, we focus on resolving the trade-off in a...

We study priority-based assignment problems with distributional and diversity objectives. Our work provides an axiomatic characterization of a general class of choice rules which are based on type-specific reserves and quotas. The choice rules in the class differ by the order in which applicants...