Good News or Bad News? Information Acquisition and Applicant Screening in Competitive Labor Markets

We model a competitive labor market with heterogeneous firms of varying productivities, and consider two information-collection processes: searching for "good news" about applicants, and searching for "bad news." Under the former, firms seek positive signals to qualify applicants, and under the latter, negative signals to disqualify them. When searching for good news, firms collect too little information in equilibrium; however, aggregate profits are positive and applicants' choice of firms is efficient. When searching for bad news, firms collect too much information, profits dissipate, and applicants inefficiently match with firms of lower productivities. In both cases, too few applicants are admitted. We show that firms tend to search for good (bad) news for low (high) revenue positions. Moreover, as the cost of acquiring information decreases, applicants' expected payoffs rise and more firms search for good news