We design and implement a large-scale field experiment on age discrimination to address limitations of past research that may bias their results. One limitation is the practice of giving older and younger applicants similar experience in the job to which they are applying, to make them “otherwise comparable.” The second limitation is ignoring the likelihood of greater variation in unobserved differences among older workers owing to human capital investment. Based on evidence from over 40,000 job applications, we find robust evidence of age discrimination in hiring against older women, but considerably less evidence of age discrimination against older men.

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 computational linguistics and machine learning methods to examine, in a field-experiment setting, ageist stereotypes that might underlie age discrimination in hiring. In so doing, we develop methods and a framework for 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, we find evidence that age stereotypes about all three categories we consider - health, personality, and skill - predict age discrimination, and for women, age stereotypes about personality predict age discrimination. In general, the evidence that age stereotypes predict age discrimination is much stronger for men, and our results for men are quite consistent with the industrial psychology literature on age stereotypes.

We design and implement a large-scale resume correspondence study to address limitations of existing field experiments testing for age discrimination that may bias their results. One limitation that may bias results is giving older and younger applicants similar experience to make them “otherwise comparable.” A second limitation is that greater unobserved differences in human capital investment of older applicants may bias the results against finding age discrimination. On the basis of over 40,000 job applications, we find robust evidence of age discrimination in hiring against older women, especially those near retirement age, but considerably less evidence of age discrimination against men.