Source: Cato Institute
by Ryan Bourne
“Most research that investigates gender wage gaps beyond the headline statistic uses snapshot data — looking at what men and women earn today, while controlling for broad variables such as education, age, occupation, experience, and demographic factors to try to see how much gap is left ‘unexplained.’ These controls help to get us closer to an apples-to-apples comparison, and some studies find including them lowers the unexplained pay gap to around 5 percent. Yet even that level of aggregation misses nuanced career decisions at the individual level, including previous shifts between occupations, specialization within a job, or significant breaks from the labor force. When such factors are ignored or oversimplified, the unexplained gap attributed to discrimination can still appear artificially large.” (06/05/25)
https://www.cato.org/commentary/new-ai-assisted-study-gender-pay-gap