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 previous 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. We find evidence that language related to stereotypes of older workers sometimes predicts discrimination against older workers. For men, our evidence points most strongly to age stereotypes about physical ability, communication skills, and technology predicting age discrimination, and for women, age stereotypes about communication skills and technology. The method we develop provides a framework for applied researchers analyzing textual data, highlighting the usefulness of various computer science techniques for empirical economics research.

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