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UN for Men Too · article
Counted by race, counted by sex, never both
There is a column for women. There is a column for ethnic minorities. Where they cross, for men, there is often no figure at all.
This is not an argument about who suffers most. It is an argument about what a table can produce. A measurement built on one axis cannot show you a group that sits on two, and the group that sits on two here is minority men.
Two columns, and the space between them
The objection this piece answers is a fair one, and it is the strongest objection this campaign faces.
The best argument against everything on this site is that men who suffer are usually suffering as poor men, minority men, migrant men, and that sex is the wrong axis to build anything on. It is a serious argument and it deserves a serious answer rather than a slogan.
Here is the answer. Nobody has to choose. The UN's own Global Study on children deprived of liberty asks states to address the overrepresentation of boys and of children from ethnic or racial minorities and from disadvantaged socio-economic groups, in one sentence, as three named axes at once.
The problem is not that anyone chose the wrong axis. It is that the instruments were built one axis at a time, and a table built on one axis cannot show you a group sitting on two.
There is a column for women. There is a column for ethnic minorities. Where those columns cross, for men, there is frequently no published figure at all.
That is a claim about statistics rather than about anybody's intentions, and the rest of this piece is three worked examples of it: one country that publishes the crossing, one service that does not, and one agency that noticed it could not ask the question and changed its own method until it could.
What the crossing shows when somebody publishes it
The United States crosses sex with race as a matter of routine, and the result is a fact no single-axis table can produce.
The Bureau of Justice Statistics releases its prisoner counts as 29 tables. Ten of them cross sex with race or Hispanic origin. Table 10 is a true cross-tabulation: sex at the top, then race within each sex.
Everyone knows prisons hold mostly men. The national ratio at the end of 2023 was about thirteen sentenced men for every sentenced woman. What a single-axis table cannot tell you is that the ratio is not the same for everyone.
Sentenced men per sentenced woman, by race
United States, state and federal, 31 December 2023. Counts from the Bureau of Justice Statistics, converted to a ratio.
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Black25 to 1
379,400 men and 15,100 women
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Asian24 to 1
14,600 men and 600 women
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Hispanic16 to 1
266,200 men and 16,500 women
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All groups13 to 1
1,124,435 men and 85,873 women
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Other10 to 1
116,300 men and 11,500 women
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White8 to 1
330,400 men and 40,100 women
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American Indian or Alaska Native8 to 1
17,500 men and 2,200 women
The sex ratio among Black prisoners is roughly three times the sex ratio among White prisoners. You cannot get that from a race table, and you cannot get it from a sex table. It only exists when both are crossed.
Say plainly what this is and is not. It is not a claim that Black men are treated worse than Black women, and it is not a ranking of suffering. It is one number that becomes visible only at the intersection, and which no amount of single-axis reporting would ever surface.
It also settles a technical question before anyone raises it. The cross-tabulation is not difficult, expensive or novel. A national statistics office publishes it every year as a matter of course.
And what happens where nobody publishes it
The UK government runs a service built specifically to show ethnic disparity. On this indicator it does not carry sex at all.
Ethnicity Facts and Figures is the British government's flagship service for exactly this kind of question. On permanent school exclusions it publishes rates by ethnicity, and it crosses ethnicity with local authority, with the reason for exclusion, and with type of school.
It does not cross it with sex. We downloaded the underlying national data file on 19 August 2026: 1,633 rows and eleven columns, none of which is sex or gender.
What that one indicator can be crossed with
Downloadable breakdowns offered for permanent exclusions on the UK Ethnicity Facts and Figures service.
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Ethnicity and local authority1 file
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Ethnicity and reason for exclusion1 file
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Ethnicity and type of school1 file
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Ethnicity and sexnot published
Ethnicity Facts and Figures, permanent exclusions, download list
Be precise about what that does and does not establish. It establishes that this service does not publish the crossing. It does not establish that no UK source does: the Department for Education presents sex and ethnicity as separate pupil characteristics for the same statistic, and its table builder may permit combining them. We could not confirm either way and we are not going to assert it.
What we can say is narrower and still worth saying. On the service a British minister, journalist or campaigner would actually open to ask about ethnic disparity in exclusions, the sex of the excluded child is not a dimension that exists.
The agency that could not ask, and then could
This is the whole argument in one federal agency, with dates, and it is not a story about concealment.
The United States Sentencing Commission has reported on demographic differences in federal sentencing for years. Its method paired race and gender and compared every pairing to one reference group: White males.
In November 2023 the Commission wrote down what that method could not do.
The Commission's prior reports analyzed demographic differences by pairing race and gender. White males were used as the baseline for comparisons with all other race-gender pairings. This approach, however, does not provide an estimate of the differences in sentences between males and females generally, or permit comparisons among females of different races.
Read that twice. The question of whether men and women receive different sentences was not refused, avoided or suppressed. It was not a quantity the model produced. Everything was measured against White males, so the comparison did not exist to be reported.
The same paragraph says what they did about it: separate baseline reference groups, so that the report could assess differences by gender while still comparing races within each sex. Then they ran it.
Sentence length, estimated effect after controls
US federal sentencing, fiscal years 2017 to 2021, multiple regression controlling for personal and offence characteristics. Positive is longer, negative is shorter.
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Female compared with male-29.2%
the comparison the previous method could not produce · p < .001
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Hispanic female vs White female27.8%
p < .001
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Black male vs White male13.4%
p < .001
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Hispanic male vs White male11.2%
p < .001
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Other female vs White female-10%
p < .05
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Black female vs White female-6%
not statistically significant
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Other male vs White male3.7%
not statistically significant
USSC, Demographic Differences in Federal Sentencing, Table 2
The largest demographic effect in the Commission's own summary table is the one its previous method could not produce. Women received sentences 29.2 per cent shorter than men after controlling for everything the Commission can observe.
Now the part that matters most, and the part a careless campaign would get wrong. Nobody hid this. The Commission identified the limitation in its own method, published it, fixed it and reported the result. That is what a statistical agency is supposed to do, and it is exactly why this example is usable and an accusation would not be.
It is also the cheapest possible answer to the objection that adding a measurement is a burden. The change here was a reference group.
The argument for a men's column is not that men suffer more than women, and it is not that sex matters more than race. It is that a column is how a thing becomes countable, and things that are not counted are not funded, not targeted and not fixed.
Minority men sit at the crossing of two axes that are both measured, separately, all the time. The crossing is where they disappear, and it reappears the moment somebody publishes the table.
There is a UN Women. There is no UN Men. This is the ask, and it takes one tap.
— voices so far · one tap, anonymous, no signup
Questions this article answers
Do minority men get worse outcomes than minority women?
On some measures, in some countries, by large margins. In US prisons at the end of 2023 there were 379,400 sentenced Black men and 15,100 sentenced Black women, a ratio of about 25 to 1, against roughly 13 to 1 for the prison population as a whole (BJS, Prisoners in 2023, Table 10). The point of this article is narrower: that ratio only exists because somebody published the crossing.
Isn't male disadvantage really about race and class rather than sex?
Both, and the UN's own Global Study on children deprived of liberty names all three axes in a single recommendation. The instruments are the problem, not the framing: a measurement built on one axis cannot show a group that sits on two.
Does the UK publish school exclusions by ethnicity and sex?
Not on Ethnicity Facts and Figures, the government's flagship service for ethnic disparity. It offers exclusions by ethnicity crossed with local authority, with reason, and with type of school. The underlying national data file has no sex or gender column at all. Whether the Department for Education's separate table builder permits the crossing, we could not confirm.
Do men receive longer sentences than women for similar offences?
In US federal courts, after controls, yes. The US Sentencing Commission estimates that women received sentences 29.2 per cent shorter than men after controlling for personal and offence characteristics (FY2017-2021, p < .001). It is the largest demographic effect in the Commission's own summary table.
Why did nobody report that before?
Because the Commission's earlier method compared every race and gender pairing to White males, which, in its own words, does not provide an estimate of the differences in sentences between males and females generally. It changed the reference groups in 2023 and the estimate appeared.
Where this goes, and what would settle it
The straightforward test is whether the Department for Education's table builder permits crossing ethnicity with sex for exclusions. If it does, the third section of this piece narrows to a statement about one service rather than one country, and we will amend it and say so.
The second test is a count nobody publishes: how many national statistical products report an indicator by sex, and by ethnicity or migrant background, and by both at once. We are not going to assemble that from impressions.
The ask that follows is small and specific. Where a statistics office already publishes an indicator by sex and separately by ethnicity, publish the crossing. It is one more table from data already collected, and the US example shows it is routine.
The two documents this rests on
Both are UN documents, both are public, and both are linked here directly rather than described. Read them yourself. That is the point of putting them at the top of their own section instead of at the bottom of a reference list.
BJS Prisoners in 2023, Statistical Tables (NCJ 310197) September 2025 Open the PDF on un.org → USSC Demographic Differences in Federal Sentencing November 2023 Open the PDF on un.org →Source notes and accuracy flags
This section is part of the article, not an appendix to it. It records where each figure came from, what was checked, and every place where the honest version of a claim is weaker than the version that would have read better.
Source notes
- Every figure here was taken from the publishing body's own data file rather than from a summary. The BJS ratios are computed from the counts in Table 10 of Prisoners in 2023. The UK finding comes from downloading the permanent exclusions national file and reading its column headers. The sentencing figures are quoted from Table 2 of the Commission's report and the methodology paragraph is quoted verbatim. Working files are in the repository under research/.
Every figure in this article also appears in the register, with its exact value and primary source, and any correction to it will appear in the corrections log with the date it was made.