{"id":610,"date":"2026-07-22T11:40:11","date_gmt":"2026-07-22T11:40:11","guid":{"rendered":"https:\/\/www.vaultinsider.top\/?p=610"},"modified":"2026-07-22T11:40:11","modified_gmt":"2026-07-22T11:40:11","slug":"stanford-and-adp-have-new-payroll-data-on-gen-z-womens-job-struggles-ai-isnt-the-main-culprit","status":"publish","type":"post","link":"https:\/\/www.vaultinsider.top\/?p=610","title":{"rendered":"Stanford and ADP have new payroll data on Gen Z women’s job struggles. AI isn’t the main culprit"},"content":{"rendered":"
<\/p>\n
Young women are falling behind young men in the entry-level job market \u2014 but it\u2019s not primarily artificial intelligence that\u2019s to blame, according to the researchers behind one of the country\u2019s most closely watched labor-market trackers. The finding comes from new data shared exclusively with Fortune<\/em> ahead of its public release tomorrow.<\/p>\n
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The data comes from an update to the \u201cCanaries Dashboard,\u201d a joint project of the Stanford Digital Economy Lab and ADP Research, led by Erik Brynjolfsson and Nela Richardson that documents how generative AI is reshaping entry-level hiring. Since its debut, the dashboard has established a clear and unsettling pattern: workers age 22 to 25 in the most AI-exposed occupations, like software development and customer service, have seen employment decline sharply since ChatGPT\u2019s late-2022 debut, even as overall U.S. employment growth has stayed healthy. That divergence hasn\u2019t reversed \u2014 it\u2019s deepened steadily, growing by roughly half a percentage point per month since the researchers\u2019 original paper last year.<\/p>\n
The latest update breaks the numbers down by gender for the first time, and young women in the Canaries sample do show weaker employment growth than young men in the same 22-to-25 age bracket. The researchers found two forces working at once: women are more likely to work in AI-exposed occupations to begin with, and, separately, they see somewhat slower employment growth than men at every level of AI exposure.<\/p>\n
Most of the gap traces back to that occupational mix: 43.8% of women work in the most-exposed category of jobs and 21.2% in the second-most-exposed, compared with 32.4% and 18.1% of men.<\/p>\n
But when the researchers isolated how much of the growth gap tracked specifically with AI exposure, the connection wasn\u2019t there. Women\u2019s slower growth shows up in low-exposure jobs almost as much as in high-exposure ones**. In** the least-exposed quintile, employment among 22-to-25-year-old women grew just 1.3% a year after late 2022, compared with 2.7% for men \u2014 a gap nearly as wide as in the most-exposed category, where women\u2019s employment shrank 4.5% a year against 2.5% for men.<\/p>\n
\u201cThese gaps are a feature of our broader sample; they are not noticeably correlated with AI exposure,\u201d the researchers wrote in materials shared with Fortune<\/em>. Their conclusion: \u201cGender-based differences in the relationship between AI exposure and employment trends appear to be driven primarily by occupational composition, rather than disparate trends within given sets of occupations.\u201d<\/p>\n\n
<\/div>\n<\/figure>\n
Why that distinction matters<\/h2>\n
It would be easy to read the headline composition numbers \u2014 women twice as likely as men to work in high-exposure jobs \u2014 and assume AI is the story. Prior research has pointed that way: the International Labour Organization has found women\u2019s jobs nearly twice as likely as men\u2019s to be exposed to generative AI, and separate estimates have put women at three times the automation risk of men.<\/p>\n
What the Canaries data adds is something those exposure-based studies couldn\u2019t measure: actual, realized employment outcomes, tracked month by month across 4.6 million workers and more than 730 occupations. If AI exposure were driving the gender gap, the gap should widen sharply as exposure rises. It doesn\u2019t \u2014 it\u2019s roughly the same whether a job is barely touched by AI or squarely in its path, which is precisely why the research team is looking elsewhere for an explanation.<\/p>\n
A story about labor market structure, not AI treatment<\/h2>\n
The finding doesn\u2019t undercut the dashboard\u2019s broader thesis about entry-level hiring \u2014 if anything, it sharpens it. Brynjolfsson and Richardson\u2019s core argument has been that AI is disrupting tasks before it disrupts jobs, hammering the mechanical, easily automated work \u2014 summarizing, formatting, scheduling \u2014 that typically gets handed to the newest employees on a team, regardless of who they are. Richardson has framed the automation-versus-augmentation distinction as the real variable: occupations where AI augments human work show durable employment growth, while those where it automates tasks outright are contracting \u2014 and early-career roles sit disproportionately in that second group.<\/p>\n
Women\u2019s overrepresentation in AI-exposed roles means they\u2019re more exposed to that dynamic, too \u2014 but as a byproduct of occupational sorting that predates generative AI by decades, not because AI treats women differently once they\u2019re in a given job. What\u2019s actually behind women\u2019s flatter growth curve across the board \u2014 education mix, industry concentration, hours worked, return-to-office effects \u2014 is a question further research is expected to answer over time.<\/p>\n<\/div>\n
Young women are falling behind… <\/p>\n","protected":false},"author":1,"featured_media":611,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[539,534,548,541,535,542,536,546,544,537,547,540,538,545,543],"class_list":["post-610","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-adp","tag-automatic-data-processing","tag-culprit","tag-data","tag-disruption","tag-gen","tag-gen-z","tag-isnt","tag-job","tag-jobs","tag-main","tag-payroll","tag-stanford","tag-struggles","tag-womens"],"yoast_head":"\n
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