Counting women: Why sex‑based data still matters (The Unmoved Feminist)

Uncategorized 6 Minutes

At first glance, “sex‑disaggregated data” sounds like a technical matter best left to statisticians. It is easy to assume that debates about whether forms record “sex” or “gender identity”, or whether reports talk about “women” or “people who experience violence”, are just questions of wording. From a second‑wave, materialist feminist perspective, they are nothing of the sort.

Feminists fought for sex‑based data because without it, we could not prove what we knew from our lives: that women, as a sex class, are underpaid, overworked, more likely to be poor in older age, and disproportionately targeted by male violence. When sex quietly disappears from official statistics, those patterns become harder to see. That does not make the inequalities go away: it makes them easier to deny.

Why second‑wave feminists insisted on counting women

The push for sex‑disaggregated data grew out of the second wave, when women were systematically documenting what had previously been treated as private misfortune. Writers such as Kate Millett in Sexual Politics argued that patriarchy is a political institution: a system of male supremacy organised through law, culture and force, not a natural order. To persuade governments—and often other women—of this, it was not enough to offer personal testimony. There had to be evidence that women’s disadvantage was patterned.

Sex‑disaggregated statistics were one of the tools that made those patterns visible. When labour force surveys began to report women’s and men’s employment and pay separately, persistent pay gaps and occupational segregation came into view. When time‑use surveys counted unpaid care and domestic work, it became clear that women were doing far more unpaid labour than men, often at the expense of their income and health.

Similarly, when crime and victimisation data identified the sex of both offenders and victims, the scale of male violence against women could be shown rather than merely asserted. Susan Brownmiller’s analysis of rape’s social function as a “conscious process of intimidation” through which all men keep all women in a state of fear depended on this kind of information: it showed that sexual violence was overwhelmingly perpetrated by men against women, not randomly distributed between sexes.

Materialist feminists such as Christine Delphy used these emerging datasets to argue that women’s unpaid work in the home forms a distinct domestic mode of production that benefits men as a class, and that women’s economic marginalisation could not be understood without taking that work into account. Sex‑disaggregated data was not a neutral add‑on. It was central to showing that women’s subordination is built into how economies and families are organised.

What happens when sex drops out

In recent years, official guidance and sector practice have shifted towards ‘gender‑neutral’ language and identity‑based categories. Forms ask for ‘gender identity’ instead of sex. Policy documents talk about ‘victim‑survivors’ and ‘people who use violence’ in place of women and men. Data on ‘LGBTIQA+ communities’ is sometimes presented as a substitute for sex‑disaggregated data.

These changes can sound inclusive. But in practice, they often make it harder to see what is happening to women as a sex class.

If criminal justice data records only ‘gender identity’, or collapses sex into broad identity categories, it becomes difficult to track patterns of male violence against women. Analysts can still say that ‘people’ are committing offences and ‘people”’ are victimised, but the fact—visible for as long as sex has been counted—that men are responsible for the overwhelming majority of sexual and domestic violence is blurred. Brownmiller’s argument that rape functions as a system of terror is harder to sustain in public debate if one can no longer show, in straightforward numbers, that men overwhelmingly rape and women overwhelmingly are raped.

The same problem arises in economics. Research on the ‘gender pay gap’ and the ‘motherhood penalty’ depends on data that distinguishes clearly between women and men, and between mothers and non‑mothers. If sex drops out of income, employment and pension statistics, older women’s poverty becomes ‘older people’s poverty’, and the fact that it disproportionately affects women—especially single women, widows and women who did unpaid care—is obscured. Feminist economists have warned for decades that unpaid care, if not counted separately, vanishes into the household and is treated as a private matter rather than a structural feature of the economy.

In other words, when sex is replaced with identity categories, data becomes more inclusive in appearance but less capable of showing the specific ways in which women, as a sex class, are disadvantaged and harmed.

Identity checklists and the loss of pattern

And there is a further, subtler shift. When categories are organised around identity labels, the central question tends to become: are all identities listed? It becomes a matter of completing a checklist. From a sex‑based, materialist perspective, the primary question is different: can we still see what is happening to women as a class?

Marilyn Frye’s ‘birdcage’ image is helpful here. In her essay ‘Oppression’, Frye describes how individual rules or expectations—a job requirement here, a dress code there—can each look trivial if examined in isolation. It is only when one steps back far enough to see the whole cage that it becomes clear why the bird cannot escape. Sex‑disaggregated data is one of the ways feminists have stepped back. It reveals the clustering of low pay, unpaid care, male violence and health inequities that shape women’s lives.

If the category of sex disappears from data, analysts and policymakers are effectively pushed back towards examining individual ‘wires’ again: isolated incidents, particular identities, discrete services. The pattern—the birdcage—slips out of view. That makes it harder to explain why women need structural responses such as sex‑based protections, female‑only spaces, or targeted services for victim‑survivors of male violence.

Intersectionality needs better sex‑based data, not less

Some argue that the move away from sex is justified by intersectionality: that focusing on sex risks ignoring race, class, disability and other axes of oppression. Kimberlé Crenshaw’s original work on intersectionality points in the opposite direction. In her analysis of legal cases, Black women were falling through the gaps because the existing categories could not capture the combined effect of racism and sexism.daily.

Crenshaw’s solution was not to drop sex as a category, but to insist on analytical tools that could register multiple dimensions at once. An intersectional approach requires more precise sex‑based data, not less. To understand, for example, how family violence affects Aboriginal and Torres Strait Islander women, migrant women or women with disabilities, one needs data that records sex and race and disability and other relevant factors. One cannot see how racism and sexism intersect if either term disappears from the measurement.

If official statistics record only identity labels or aggregate categories such as ‘marginalised communities’, the women Crenshaw was concerned about are once again obscured. The gaps intersectionality was developed to address re‑emerge under a different name.

Whose reality counts?

Catharine MacKinnon has long argued that law tends to take men’s reality as the default human reality. One of the uses of sex‑based data has been to force institutions to confront the fact that women’s reality is different in demonstrable ways. Data on male violence, on the motherhood penalty, on older women’s poverty, on women’s over‑representation in unpaid care, has provided levers for legal and policy change.

When sex is removed or downgraded in data systems, the effect is to narrow the space in which those levers can operate. Without clear evidence of sex‑based patterns, women’s experiences can once again be dismissed as anecdotal. Calls for female‑only spaces, for targeted services, or for sex as a protected characteristic in law become easier to characterise as special pleading rather than as responses to well‑documented structural realities.

Sex‑based data was a feminist demand because, without it, women could not make a serious, evidence‑based case for structural change. That remains true. If we cannot count women, we cannot centre women. If we cannot show what is happening to women as a sex class, we cannot defend the gains second‑wave feminists won, let alone extend them.

Further reading

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