There are certain jobs that we almost unfailingly associate with women. When someone mentions a nurse, we imagine a woman. Same for a teacher and a childcare worker. Now think about what they have in common, beyond the obvious. They’re all considered noble, essential, even admirable. They’re the kinds of careers people praise at dinner parties and celebrate on awareness days. Yet, almost universally, they don’t pay particularly well, and we don’t usually ask why. But when researchers started asking this question, they were shocked by the answer: when women begin entering a workforce sector in large numbers, the pay in that sector tends to fall. And the reverse is also true: when men dominate a field, it tends to pay well.

The numbers are hard to ignore

In 2009, three researchers published what became a landmark study in the journal Social Forces. They analysed 50 years of US Census data, from 1950 to 2000, and found that occupations with a greater share of female workers consistently paid less than those with a lower share. The research concluded that the evidence strongly supported “the devaluation view”, which is the idea that when women do a job, society values it less.

More recently, a 2021 paper published in the Journal of Labour Economics by researcher Matthew Wiswall went even further. Using data spanning 1960 to 2010, he found that a 10 per cent-point increase in the proportion of women in an occupation led to a nine per cent decrease in average male wages and a 14 per cent decrease in average female wages. So the more a field feminises, the more its wages drop for everyone, men included.

It’s not about the work itself

You might think that maybe women are simply choosing less demanding, lower-skilled jobs. The research rules this out. The studies control for those variables — education level, work experience, required skills, and even geography. The gender pay gap persists regardless. A striking illustration comes from the Economic Policy Institute: jobs done by women are devalued not because they’re easier or less skilled, but simply because women are doing them. As Harvard economist Claudia Goldin told the New York Times: “There is a belief, which is just not true, that women are in bad occupations and if we just put them in better occupations, we would solve the gender gap problem.”

The World Economic Forum summarised the research, stating, ‘Consider janitors versus maids’. Both jobs are almost identical in every practical respect, save for the gender that typically holds each. The difference in pay is hard to explain by anything other than who is doing the work.

The story of computer programming

No example illustrates this phenomenon better than the history of computer programming. In the 1950s and 1960s, computing was considered a natural fit for women — painstaking, detail-oriented, clerical-adjacent work. Women like Grace Hopper were pioneers in the field, but as its commercial potential became clear, men moved in. Hiring criteria changed, and the cultural stereotypes of the “geek” programmer ( male, anti-social, technical) took hold. By 1985, women held 38 per cent of computer science jobs; by 2015, that figure had dropped to just 18 per cent.

This shift led to computer programming becoming one of the most prestigious and well-paid professions in the modern economy. The inverse happened with fields like teaching and nursing. Once respectable professions for men, they became “pink-collar” jobs as women moved in, and pay stagnated accordingly.

The two theories, and which one wins

Researchers have proposed two explanations for why female-dominated occupations pay less: The queuing view says it works the other way around: low-paid jobs attract women because employers prefer men for better roles, so women end up in the less desirable positions. Under this theory, low pay causes high female representation, not the other way around.

The devaluation view argues the opposite: as more women enter a field, pay drops because the work is culturally devalued when associated with women. The 50-year longitudinal analysis by Levanon, England, and Allison found “substantial evidence for the devaluation view, but only scant evidence for the queuing view.” In other words, the gender composition of a job drives pay, not the other way around.

Paula England spent 20 years testing this hypothesis, controlling for every variable she could think of. Her fixed-effects models showed that when the same person moves from a female-dominated job to a male-dominated one, their pay goes up. When they move in the other direction, it goes down.

So what do we do about it?

Understanding the problem is the first step. If wages drop not because of the inherent nature of the work, but because of who is doing it, then the solution cannot simply be “get women into better jobs.” That advice misses the point entirely because the jobs become lower-valued precisely as women arrive.

What researchers and economists broadly suggest instead:

Comparable worth policies: paying jobs based on skill, effort, and responsibility rather than gender composition

Pay transparency: making salaries visible so that systematic undervaluing is harder to hide

Desegregating occupations: encouraging both men and women into fields traditionally associated with the other gender, which appears to reduce the devaluation effect over time

Revaluing care work: recognising that nursing, teaching, childcare, and similar fields are economically essential and currently structurally underpaid

None of these is a quick fix. The devaluation of women’s work is baked into labour market institutions, cultural assumptions, and hiring practices that have built up over decades. But the research is detailed: the problem is real, it is measurable, and it costs both women and men.

So, the next time someone suggests the gender pay gap is simply a reflection of individual choices, show them what data so clearly suggests.

Featured Image Source

More from All About Eve

40 Men In 3 Hours: What Happened When A Cop Disguised Herself As An Ordinary Woman At Night

Who Decides What’s Beautiful? Inside The Racial Bias Of AI Beauty Tools

The Algorithm Knows You’re Pregnant Even Before You Do: The Dark Truth About Your Friendly Period Tracking App

Who Trains AI To Flag Explicit Videos? Women In Rural India Who Are Sacrificing Their Mental Health For Ours

Looksmaxxing: India’s Beauty Standards Crisis Is A Men’s Problem Too