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Information and Sorting in Labor Markets

Paper Session

Sunday, Jan. 5, 2020 1:00 PM - 3:00 PM (PDT)

Marriott Marquis, Cardiff
Hosted By: American Economic Association
  • Chair: Bo Cowgill, Columbia University

Promotions and the "Peter Principle"

Danielle Li
,
Massachusetts Institute of Technology
Alan Benson
,
University of Minnesota
Kelly Shue
,
Yale University

Abstract

The best worker isn’t always the best candidate for manager. In these cases, do firms promote the best potential manager or the best worker in their current job? Using data on the performance of sales workers from 131 firms, we find evidence consistent with the Peter Principle: firms prioritize current job performance when making promotion decisions, at the expense of other observable characteristics that better predict managerial quality. We estimate that the costs of managerial mismatch are substantial, suggesting that firms are either making inefficient promotion decisions or that the incentive benefits of emphasizing current performance must also be high.

An Empirical Framework for Matching with Imperfect Competition

Kory Kroft
,
University of Toronto
Ismael Mourifie
,
University of Toronto
Mons Chan
,
University of Toronto

Abstract

This papers considers a static, many-to-one matching model of the labor market. Firms face inelastic labor supply curves and hence charge a markdown below marginal product. We assume that firms operate in an oligopsony labor market and thus allow for strategic interactions in wage setting. We provide a characterization of the equilibrium and demonstrate existence and uniqueness for a general class of many-to-one matching functions. We also illustrate identification of structural parameters and show how one may decompose earnings inequality into variation driven by human capital, Roy sorting, compensating differentials, and imperfect competition.

Incentivized Resume Rating: Eliciting Employer Preferences without Deception

Judd Kessler
,
University of Pennsylvania
Corinne Low
,
University of Pennsylvania
Colin Sullivan
,
University of Pennsylvania

Abstract

We introduce a new experimental paradigm to evaluate employer preferences for job candidates, called Incentivized Resume Rating (IRR). Employers evaluate resumes they know to be hypothetical in order to be matched with real job seekers, allowing researchers to randomly assign resume characteristics while preserving incentives and avoiding deception. We deploy IRR to investigate preferences of employers recruiting prestigious college graduates and find evidence of discrimination. Employers recruiting for science and engineering positions are less interested in hiring females and minorities. Moreover, employers believe females and minorities are less likely to accept jobs when offered, suggesting a novel channel for discrimination.

The Effect of Observation and Deception in Field Experiments: Evidence from a Two-sided Audit Study

Amanda Agan
,
Rutgers University
Bo Cowgill
,
Columbia University
Laura Gee
,
Tufts University

Abstract

One of the strengths of natural field experiments is that subjects' actions are observed while they are unaware they are in an experiment. This might be particularly important for actions that are affected by implicit biases. One common natural field experimental design is the use of resume audit studies. Audit studies are sometimes criticized because they deceive subjects and do not allow subjects to consent to participate. An important question is whether the subject being aware they are in an experiment and dealing with fictitious applicants actually changes their behavior. We leverage a unique setting where we elicited responses from real recruiters about fictitious job applicants both with the deception usual of audit studies and with informed consent where the recruiters knew this was part of an experiment. We show that results differ when recruiters are aware they are taking part in an experiment and the subjects are fictitious.
Discussant(s)
Kathryn Shaw
,
Stanford University
Zoe Cullen
,
Harvard Business School
William Kerr
,
Harvard Business School
John Michael Van Reenen
,
Massachusetts Institute of Technology
JEL Classifications
  • J3 - Wages, Compensation, and Labor Costs
  • M5 - Personnel Economics