Men are experiencing more job growth than women in AI-exposed occupations
B · Useful, limited Pew Research Center analysis of Current Population Survey data with a stated method; credible and checkable, but a simple before-after association with no controls for other changes.
Summary based on the full text·Original: about 6 minutes to read
In short
A Pew Research Center short read (22 October 2026) by Nicole Smith. Comparing 2017-19 with January 2023 to June 2026, employment in more AI-exposed US occupations grew for men but barely changed for women. The gap is driven by workers without a bachelor's degree; for graduates, neither men's nor women's employment changed significantly with exposure. Pew stresses the link is not necessarily causal.
Key numbers
- +6.3% vs +0.2%men's vs women's employment growth per 1-point increase in occupational AI exposure, 2017-19 to 2023-26
- +8.8% vs -9.5%same measure for men vs women with a high school diploma or less
- ~24%extra employment growth for men in the most vs least AI-exposed occupations
- 480occupations scored for AI exposure on a 0 to 3.5 scale
As reported by the source.
English·Web page·opens pewresearch.org
What it reports
- For each 1-point increase in an occupation's AI exposure score, men's employment grew 6.3% and women's 0.2%.
- Men's employment grew about 24% more in the most exposed occupations than in the least; women's growth was about the same across the scale. In the least exposed occupations, women's employment grew faster than men's.
- High school or less: men +8.8% per point, women -9.5%. Some college: a smaller but significant gap. Bachelor's or higher: no significant change for either.
- Men without degrees are concentrated in low-exposure jobs (truck driving, construction labour, freight), so their growth in exposed occupations covers a small share of them. Women without degrees often work as secretaries, receptionists and customer service representatives, in the middle to upper part of the exposure scale.
- Employment in the most exposed occupations has grown overall, not shrunk.
How they got it
CPS basic monthly files for 2017-19 and January 2023 to June 2026, excluding 2020-22. An exposure score for 480 occupations based on whether each O*NET task can be performed or assisted by generative AI, weighted by task importance. Within each occupation, Pew compared change in the number of employed men and women, then tested whether the gap widens with exposure.
Limits
- Association only; other changes over the period may explain the pattern.
- The exposure score is relative (0 means least exposed in the data, not unexposed) and Pew's own construction.
- No controls for industry, region or the business cycle are reported in the short read.
Why it matters for companies
The finding points at a specific group: women without degrees in clerical and customer-facing roles that AI tools increasingly handle. Firms automating front-office and support work should check who holds those roles and what redeployment paths exist, since the effect does not show up in graduate roles. It also adds a gender dimension to workforce-impact reporting that headcount totals hide.
Summary based on the full text. Read the original for the full argument and data.Read the original on pewresearch.org
- Tags
- Labour market