Working Paper: NBER ID: w26552
Authors: Ian Burn; Patrick Button; Luis Felipe Munguia Corella; David Neumark
Abstract: We study the relationships between ageist stereotypes – as reflected in the language used in job ads – and age discrimination in hiring, exploiting the text of job ads and differences in callbacks to older and younger job applicants from a resume (correspondence study) field experiment (Neumark, Burn, and Button, 2019). Our analysis uses methods from computational linguistics and machine learning to directly identify, in a field-experiment setting, ageist stereotypes that underlie age discrimination in hiring. The methods we develop provide a framework for applied researchers analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, our evidence points to age stereotypes about all three categories we consider – health, personality, and skill – predicting age discrimination, and for women, age stereotypes about personality. In general, the evidence is much stronger for men, and our results for men are quite consistent with the industrial psychology literature on age stereotypes. \n\n\n\n\n
Keywords: age discrimination; job ads; ageist language; hiring discrimination; machine learning
JEL Codes: J14; J23; J7; J78
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| language in job ads (J68) | callback rates for older applicants (J14) |
| negative stereotypes in job ads (J71) | lower callback rates for older applicants (J14) |
| health stereotypes in job ads (I11) | higher discrimination rates for middle-aged men (J79) |
| positive stereotypes in job ads (J68) | lower discrimination rates against older women (J71) |
| ageist stereotypes in job ads (J71) | hiring discrimination (J71) |