Working Paper: NBER ID: w28328
Authors: Ian Burn; Daniel Firoozi; Daniel Ladd; David Neumark
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.
Keywords: Age Discrimination; Job Ads; Machine Learning; Age Stereotypes
JEL Codes: J14; J71; K31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| job ad language classified as ageist (J14) | perceived as ageist by respondents (J14) |
| job ad language classified as ageist (J14) | discouragement of older workers from applying (J78) |
| perceived as ageist by respondents (J14) | discouragement of older workers from applying (J78) |
| job ad language classified as ageist (J14) | affects hiring outcomes for older applicants (J78) |