An agent engages in sequential search. He does not directly observe the quality of the goods he samples, but he can purchase signals designed by profit maximizing principal(s). We formulate the principal-agent relationship as a repeated contracting problem within a stopping game, and characterize the set of equilibrium payoffs. We show that when the agent's search cost falls below a given threshold, competition does not impact how much surplus is generated in equilibrium nor how the surplus is divided. In contrast, competition benefits the agent at the expense of total surplus when the search cost exceeds that threshold. Our results challenge the view that monopoly decreases market efficiency, and moreover, suggest that it leads to more information provision than does competition.

Equal pay laws increasingly require that workers doing "similar" work are paid equal wages within firm. We study such "equal pay for similar work" (EPSW) policies theoretically and test our model's predictions empirically using evidence from a 2009 Chilean EPSW. When EPSW only binds across protected class (e.g., no woman can be paid less than any similar man, and vice versa), firms segregate their workforce by gender. When there are more men than women in a labor market, EPSW increases the gender wage gap. By contrast, EPSW that is not based on protected class can decrease the gender wage gap.

Can a school-choice clearinghouse generate a stable matching if it does not allow students to express preferences over peers? Theoretically, we show stable matchings exist with peer preferences under mild conditions but finding one via canonical mechanisms is unlikely. Increasing transparency about the previous cohort's matching induces a tâtonnement process wherein prior matchings function as prices. We develop a test for stability and implement it empirically in the college admissions market in New South Wales, Australia. We find evidence of preferences over relative peer ability, but no convergence to stability. We propose a mechanism improving upon the current assignment process.

Mechanisms used to derive optimal allocations are generally designed under the premise that agents fully know their preferences. It is often impossible to duplicate these optimal allocations when agents imperfectly observe object characteristics. I present a crowdsourcing mechanism to approximate optimal allocations under imperfect observations. To ensure truth-telling, agents are punished when their reports differ from the "wisdom of the crowd." Under mild conditions, this crowdsourcing-with-punishment mechanism replicates the full-information optimal allocation with probability exponentially converging to one in the size of the market, with aggregate worst-case waste (punishment) converging exponentially to zero. I show that no other mechanism can meaningfully improve upon the proposed mechanism. The approach I take is general, and can be applied in many settings, including two-sided matching markets.