This paper deals with the issue of screening. It focuses on a decision maker who, based on noisy unbiased assessments, screens elements from a general set. Our analysis shows that stricter screening not only reduces the number of accepted elements, but possibly reduces their average expected value. We provide a characterization for optimal threshold strategies for screening and also derive implications to cases where such screening strategies are suboptimal. We further provide various applications of our results to credit ratings, auctions, general trade, the Peter Principle, and affirmative action.
- Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
- Trade: General
- Investment Banking; Venture Capital; Brokerage; Ratings and Ratings Agencies
- Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination