A firm selects applicants to hire based on hard information, such as a test result, and soft information, such as a manager's evaluation of an interview. The contract that the firm offers to the manager can be thought of as a restriction on acceptance rates as a function of test results. I characterize optimal acceptance rate functions both when the firm knows the manager's mix of information and biases and when the firm is uncertain. These contracts may admit a simple implementation in which the manager can accept any set of applicants with a sufficiently high average test score.

Many US colleges now use test-optional admissions. A frequent claim is that by not seeing standardized test scores, a college can admit a student body it prefers, say, with more diversity. But how can observing less information improve decisions? This paper proposes that test-optional policies are a response to social pressure on admission decisions. We model a college that bears disutility when it makes admission decisions that “society” dislikes. Going test optional allows the college to reduce its “disagreement cost.” We analyze how missing scores are imputed and the consequences for the college, students, and society. (JEL I23, I28)

We study a model of signaling in which agents are heterogeneous on two dimensions. An agent’s natural action is the action taken in the absence of signaling concerns. Her gaming ability parameterizes the cost of increasing the action. Equilibrium behavior muddles information across dimensions. As incentives to take higher actions increase—due to higher stakes or more manipulable signaling technology—more information is revealed about gaming ability, and less about natural actions. We explore a new externality: showing agents’ actions to additional observers can worsen information for existing observers. Applications to credit scoring, school testing, and web searching are discussed.