The Kubera Principle
The Book That Will Change the World
December 13, 2023
Hoover Institution | Stanford University
David Neumark, Hoover visiting fellow and distinguished professor of economics and codirector of the Center for Population, Inequality, and Policy at the University of California-Irvine, discussed “Help Really Wanted? The Impact of Age Stereotypes in Job Ads on Applications from Older Workers,” a paper with Ian Burn (University of Liverpool), Daniel Firoozi (Claremont McKenna College), and Daniel Ladd (Quantitative Economic Solutions, LLC).
PARTICIPANTS
David Neumark, John Taylor, Uschi Backes-Gellner, Eric Bettinger, Patrick Biggs, Michael Boskin, John Cochrane, Bradley Combest, Steven Davis, Andy Filardo, Shanon Fitzgerald, Bob Hall, Rick Hanushek, Robert Hodrick, Ken Judd, Matthew Kahn, Tom Kulisz, David Laidler, Ross Levine, Michael Melvin, Casey Mulligan, Vinh Nguyen, Elena Pastorino, Valerie Ramey, Alison Schrager, Richard Sousa, Tom Stephenson, Jack Tatom
ISSUES DISCUSSED
David Neumark, Hoover visiting fellow and distinguished professor of economics and codirector of the Center for Population, Inequality, and Policy at the University of California-Irvine, discussed “Help Really Wanted? The Impact of Age Stereotypes in Job Ads on Applications from Older Workers,” a paper with Ian Burn (University of Liverpool), Daniel Firoozi (Claremont McKenna College), and Daniel Ladd (Quantitative Economic Solutions, LLC).
John Taylor, the Mary and Robert Raymond Professor of Economics at Stanford University and the George P. Shultz Senior Fellow in Economics at the Hoover Institution, was the moderator.
PAPER SUMMARY
Correspondence studies have found evidence of age discrimination in callback rates for older workers, but less is known about whether job advertisements can themselves shape the age composition of the applicant pool. We construct job ads for administrative assistant, retail, and security guard jobs, using language from real job ads collected in a prior large-scale correspondence study (Neumark et al., 2019a). We modify the job-ad language to randomly vary whether the job ad includes ageist language regarding age-related stereotypes. Our main analysis relies on computational linguistics/machine learning methods to design job ads based on the semantic similarity between phrases in job ads and age-related stereotypes. In contrast to a correspondence study in which job searchers are artificial and researchers study the responses of real employers, in our research the job ads are artificial and we study the responses of real job searchers.
We find that job-ad language related to ageist stereotypes, even when the language is not blatantly or specifically age-related, deters older workers from applying for jobs. The change in the age distribution of applicants is large, with significant declines in the average and median age, the 75th percentile of the age distribution, and the share of applicants over 40. Based on these estimates and those from the correspondence study, and the fact that we use real-world ageist job-ad language, we conclude that job-ad language that deters older workers from applying for jobs can have roughly as large an impact on hiring of older workers as direct age discrimination in hiring.
To read the paper, click the following link
https://www.hoover.org/sites/default/files/2023-12/Stereotyped%20job%20ads%20experiment%20Hoover%20Dec%202023_0.pdf
To read the slides, click here
https://www.hoover.org/sites/default/files/2023-12/Age%20disc%20experiment%20–%20fall%202023%20–%20Hoover%5B1%5D%20%20-%20%20Read-Only.pdf
