The field-experiment evidence on age discrimination in hiring, which resume signals reveal age, and how large the documented callback gaps actually are.
Older job seekers routinely report that their applications vanish without a reply, and the suspicion is that age is the reason. Unlike most hiring-bias claims, this one rests on unusually strong evidence: the largest resume-audit study of age discrimination sent more than 40,000 otherwise-identical applications and measured exactly how callback rates fell as the applicant got older. This page collects what that experiment and the supporting survey and enforcement data establish — and where the effects are strongest. The short version is that age discrimination in callbacks is real, measurable, and most severe for older women, while the evidence for older men is weaker and more occupation-dependent.
Neumark, Burn, and Button sent triplets of identical young (29-31), middle-aged (49-51), and older (64-66) applications to over 13,000 positions across 11 states, totaling more than 40,000 applicants. Pooling occupations, callback rates were about 18% lower for middle-aged and about 35% lower for older applicants relative to the youngest group.
The discrimination was sharpest for women. For administrative jobs, older female applicants had a 47% lower callback rate (7.6% vs 14.4%). In retail sales, older women were called back 18.4% of the time against 28.7% for the youngest applicants — a 36% gap.
The authors found robust discrimination against older women but considerably less clear evidence for men. Older men in retail sales did show a statistically significant 30% lower callback rate, but in security and janitorial roles the patterns were murkier and several differences were not statistically significant. This asymmetry is one of the study's most replicated findings.
A resume rarely states age, but it leaks it. The degree's graduation year, the total span of dated work history, and how dated the oldest listed job is together let a human or a screening filter estimate age within a few years. This is why the standard advice for older candidates is to drop graduation years and trim experience older than roughly 10-15 years.
AARP's surveys of workers aged 50 and over consistently find a large majority perceive age discrimination as common; its 2024 research put the share seeing or experiencing it at roughly six in ten. In its 2022 survey, 15% said their age had prevented them from getting a job they applied for within the prior two years.
Sources: [5]
The EEOC's 50-year ADEA retrospective documents structural shifts in who files: women surpassed men in age charges for the first time in 2010, and by 2017 workers 55-64 filed more charges than younger cohorts. Charge volume confirms the problem persists; it does not, on its own, measure hiring discrimination, which is what the audit studies are for.
Sources: [6]
We prioritize the published resume-correspondence experiment (Journal of Political Economy, 2019; NBER working paper, 2015) over secondary summaries, and cross-check its figures against the Federal Reserve Bank of San Francisco's Economic Letter on the same data. Survey perceptions (AARP) and enforcement data (EEOC) are reported as what they are — self-reported attitudes and charge volumes — not as direct measures of hiring discrimination. Where a popular statistic could not be traced to a primary source, we left it out.
The audit evidence is strong enough to act on. If you are an older applicant — especially a woman near retirement age — the callback gap is real and not in your head. You cannot fix an employer's bias, but you can stop volunteering your age: remove graduation years, cap detailed experience at the most recent 10-15 years, lead with current and relevant skills rather than a decades-long chronology, and let a modern, achievement-focused summary carry the resume. None of this is deception; it is declining to hand a biased screen the one data point it most wants.
Bertrand-Mullainathan, the 2023 NBER replication, age discrimination audits, and what the field-experiment literature actually shows about hiring bias.
Field experiments on the resume-gap penalty, ATS gap filters, and survey data on shifting attitudes — separating what employers say from what they do.
The famous "6 seconds" claim, where it comes from, what the eye-tracking data actually measured, and how to write a resume for the way recruiters really read.
How many resumes really get rejected before a human sees them, who's running the systems, and what the published data does — and doesn't — say.
The data shows what works. Build the resume that benefits from it.