To somewhat play Devil's Advocate, I would highlight that the stats don't show who commits more crime, but who gets caught more. If no-one arrests you, (or if a court finds you not guilty), you won't be in the stats.

In my country, for instance, police can stop and search you if they have "reasonable suspicion" that you're carrying something illegal (stolen good, drugs, weapons etc.). If police are operating under the assumption that men commit more crime than women, they're far more likely to be suspicious of a man committing the same crime as a woman.

I haven't read anything that's about gender bias specifically at this level of policing, but I do know there's a lot of research (especially in the US) on how racial bias causes black neighbourhoods to be more heavily policed than neighbourhoods with comparable crime levels, leading to a self-reinforcing cycle where heavier policing leads to increased belief that black people commit more crime, which leads to heavier policing^[1][2]

I do know that after an arrest has been made, women tend to fare better than men; they are less likely to be sentenced, and when they are, they tend to receive less severe sentences than men, even for equivalent crimes^[3][4] . This is speculative, but I imagine this has a cascading effect — if there is a crime where the punishment could range from community service to a prison sentence, then the person who gets community service is statistically less likely to reoffend than the person who goes to prison^[5]

All that in mind, I'm pretty confident that the gender ratio in crime statistics gives a skewed impression of who actually commits more crime, and that women are effectively undercounted if we're talking about who commits more crime — though I can't guess on to what degree this is the case. However, it's entirely possible that women commit crimes at a similar rate to men, or even at a higher rate. We can't really know.

And to finish off this comment with a slightly more shitposty answer that still links into my broader point, it's possible that women commit as much crime as men, but the statistics are skewed towards men because women are more effective criminals.

I include this last possibility because I am uncomfortable with how you framed things in your question, with phrases like "crime being disproportionately committed by men is a universal constant". Statistics are never Truth, and are, at best, only ever an approximation. Stats can give us a sense of clarity in an overwhelming world by reducing down complexity into much more easily parsed, quantitative data, often presented in an easy to visualise manner. It feels objective. However, statistics only serve to mask the underlying bias in what data we choose to collect, who collects it, and how — which means that treating statistics as objective can be dangerous due to making us less aware of biases and inequality, and thus even less objective.

I like the way that the feminist philosopher Donna Haraway puts it; she describes data visualisations as "the god trick of seeing everything from nowhere"^[4][5]. I'mma quote a long passage from an excellent book here, because I don't think I can explain it any better than this:

"The view from nowhere—from a distance, from up above, like a god—may be data visualization’s most signature feature. It’s also the most ethically complicated to navigate for the ways in which it masks the people, the methods, the questions, and the messiness that lies behind clean lines and geometric shapes. Haraway calls it a trick because it makes the viewer believe that they can see everything, all at once, from an imaginary and impossible standpoint. But it’s also a trick because what appears to be everything, and what appears to be neutral, is always what she terms a partial perspective. And in most cases of seemingly “neutral” visualizations, this perspective is the one of the dominant, default group." ^[5]

To bring things back to our question, I strongly believe that we don't know if men commit more crime than women. I think it's plausible that it could be true, but due to inherent bias in how the data behind these statistics are gathered (i.e. documented inequalities in policing and sentencing), we simply don't know. Statistics always carries this problem of bias being hidden in the data, but it's especially tricky when dealing with complex socioeconomic matters such as crime. To me, this is a standout example of an area where we need to be especially cautious that we don't mistake the stats for truth.

[1]: Open Access Academic Paper:
"Smartphone Data Reveal Neighborhood-Level Racial Disparities in Police Presence", (2023), Chen et al.
https://doi.org/10.1162/rest_a_01370

[2]: More accessible summary of [1]
https://anderson-review.ucla.edu/smartphone-records-reveal-racial-disparities-in-neighborhood-policing/

[3]: Paywalled Academic Paper:
"Gender Disparities in Sentencing", (2020), Arnaud Philippe
https://doi.org/10.1111/ecca.12333
Unpaywalled SciDB mirror via Anna's Archive

[4]: More accessible summary of [3], by Michelle Kilfoyle and Arnaud Philippe
https://ceps.blogs.bristol.ac.uk/2021/11/17/gender-stereotypes-see-female-criminals-fare-better-in-court/

[5]: Old Academic Paper:
“Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective,” Feminist Studies 14, no. 3 (1988): 575–599
Quote and reference retrieved via [6]

[6]: Open Access Book "Data Feminism", (2020), Catherine D'Ignazio and Lauren Klein
Fairly academic, but also quite accessible to anyone interested in how socioeconomic inequality shapes how we use data, and how data feeds inequality. I highly recommend this book, it is excellent
Quoted section found here

And on the off chance one of you delightful nerds would like to read more, here is a link to the main book page, for your convenience:
https://data-feminism.mitpress.mit.edu/

^(It's funny that now I'm no longer in academia, I seem to have fun writing cited essays. Though to be fair, I studied biochemistry, so this is outside of my main wheelhouse — which is probably why I'm so diligent with citing my claims)