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Stereotypes of older workers and perceived ageism in job ads: evidence from an experiment

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  • Burn, Ian
  • Firoozi, Daniel
  • Ladd, Daniel
  • Neumark, David

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 searching for jobs. We find that job-ad language classified by the machine-learning algorithm as closely related to ageist stereotypes is perceived by experimental subjects as biased against older job seekers. 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.

Suggested Citation

  • Burn, Ian & Firoozi, Daniel & Ladd, Daniel & Neumark, David, 2023. "Stereotypes of older workers and perceived ageism in job ads: evidence from an experiment," Journal of Pension Economics and Finance, Cambridge University Press, vol. 22(4), pages 463-489, October.
    • Handle: RePEc:cup:jpenef:v:22:y:2023:i:4:p:463-489_2
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