I don't see how a number cribbed from a comparison of "work day length" (NOT total hours worked!) across the entire US workforce (and thus not correcting for the fact that women and men have different career statistics) says anything about whether women in the tech industry covered by this glassdoor study work more or less hours than their coworkers.
What you did is go out through the whole world of statistics to find one value that kinda/sorta "fixes" this inconvenient finding in the direction you want. That's just baldly ridiculous cherry picking, and you should be ashamed.
What? That's 100% up-is-downism. What you say is simply incorrect. The conclusions is backed by data. The data is in the link. Go look at it. See? Glass Door says women are underpaid. That's data!
What you are doing is saying "No, I don't think the conclusion should be what the data says", and then you're going out and finding ("cherry picking") different data about related but not identical subjects that appears to contradict it.
Sorry, but the burden of proof is on you if you want to make a numeric argument here. And you're doing it with extraordinarily bad analysis.
Even if the data says women are paid less, it doesn't say why. Correlation does not show causation. The implication is that there is gender discrimination. Research by women into the gender wage gap has explored a large number (but not all) reasons for this and shown the actual gender wage gap when taking into account actual reasons is only as high as 8%, with more research and experimentation underway to account for additional reasons. This is the most current objective research on the subject I am aware of.
"New research by ILR School professors Francine Blau and Lawrence Kahn finds an eight percent gender wage gap that cannot be accounted for, even after controlling for observable variables that influence workers’ pay.
Gender discrimination in the workplace COULD be a cause, they suggest."
Still better than "Hey guyz! I found a link on Forbes to a BLS study about some other subject that totally makes you wrong!"
Believe what you want. Just like climate change denial, there's enough uncertainty in these numbers to make any level of ostrich-headery justifiable. I just want to know why no one ever manages to show data that is "wrong" in the other direction...
What you did is go out through the whole world of statistics to find one value that kinda/sorta "fixes" this inconvenient finding in the direction you want. That's just baldly ridiculous cherry picking, and you should be ashamed.