Published Papers


Working Papers

"Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment"

(with Ian Burn, Daniel Ladd, and David Neumark) [NBER Working Paper 28328]

Abstract: We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.

Works in Progress

  • "Public Research Universities and Political Action" (Sole Author)  [Job Market Paper]

  • "Stereotyped Job Ads and Decisions by Older Workers to Apply for a Job" (with Ian Burn, Daniel Ladd, and David Neumark)

  • "Non-resident Tuition and Student Achievement: Evidence from Randomized Fee Vouchers" (Sole Author)

  • "The Civic Returns to Financial Aid" (with Igor Geyn)