Both things can be true.
To use an exaggerated example, if you pay the top 20% of people six figures and leave the bottom 80% to starve, the average earnings would look great, but you’ll soon have an angry mob on your hands.
Now imagine if your average man used to be able to do skilled factory work and support a family, but those jobs have gone and growth sectors like care don’t pay enough to support a family.
Then imagine an electoral system where a 4% margin separates the winner and the loser. Doesn’t take 80% of the population being disaffected to flip the results.
- 70% of men being allowed to get 20$ and 30% being excluded from this opportunity and getting only 5$
- 100% of women being excluded from the opportunity to get 20$ and getting only 12$
Sure, the excluded women gets more than the excluded men, but it is also very unfair that men have 70% chance to "make it" while women have 0% chance to "make it".
Not saying one is worse than the other (and it is illustrative numbers anyway), but just to illustrate that 1. in both cases, looking at only one metric is not enough, 2. at the end, the answer is not really "objective" or "mathematical", and two persons can reach different conclusions based on their values.
There are countless other statistics that paint a clear picture that men are struggling. At what point will you actually care?
It is very clear that public image has a huge impact on what people choose. For example, people who consider themselves introvert choose, in majority, to avoid fields that have a strong extrovert vibe. Similarly, people will tend to not choose fields if the field "gives a vibe" they don't feel they belong to. So, if there is an initial bias toward men, the fact that some people don't choose the field is in no way a proof that there is no bias.
I agree that the 10% number is not the best, but the "corrected" number where you take the samples in same job and position does the same mistake. In fact, there are arguments that in these cases, you have a selection bias (some of the men in the field are seeing this field as their calling, but some of the men are just doing it as a job without being overly passionated, while the women that are not overly passionated just don't choose this job) and that using this methodology, women should overperform because there is a gap. The "real" number is probably in between.