Google found it paid men less than women for the same job
techcrunch.com
techcrunch.com
So this could mean that women stay at lower job titles longer.
It could also mean that they sign into the company with higher base salaries. I have seen that maybe happen once in my experience for the same experience level but I have no way of correcting that for their interview performance or how well they negotiated.
Basically I don’t think this data point is super useful on its own.
The company’s annual analysis only compares employees in the same job category, so the results do not reflect race or gender differences in hiring and promotion.
https://www.wired.com/story/men-google-paid-less-than-women-...
Note that 'diverse' is quoted here because the whole corporate diversity enterprise is utterly superficial or it wouldn't categorize individuals as 'diverse' or 'not diverse' and it wouldn't choose the most superficial criteria (race, gender, etc as opposed to viewpoint, perspective, or cultural diversity) to make those assessments.
https://cis.org/North/H4s-Data-Shows-MirrorLike-Strong-Biase...
[1]: https://h1bdata.info/index.php?em=GOOGLE+INC&job=SOFTWARE+EN...
The ranges show that they're often higher than the minimum for the role.
I'd love you to prove me wrong. If Google has domestic talent available at $150k, and can pay $110k to an h1b, they are supposed to hire the domestic talent. That's how the system works (or rather, it does if a company doesn't abuse it). I'd love to find out they are instead having to pay the h1b $175k because no domestic talent is available. If Google is doing this, and is the statistical anomaly, then good on them. However, all the evidence I've seen (anecdotally and in writing) suggest otherwise (though at an industry-wide level, not at the Google level)
You can't just use a statistical average to make a conclusion about only one of the data points. That's stats 101.
A better comparison might be to trace the trajectories of men and women who started out in similar jobs and at similar pay levels, and the see how many women appear overpaid.
I bet you won't find too many.
There's also another likely contribution to higher pay for women: a shorter supply of them, and Google bidding to hire/keep them versus other companies.
This is less than $1,000 per employee that was adjusted. Given the salaries at Google, this is almost certainly a <1% difference and the pay bump is pretty much negligible. It's less than the cost of living adjustment that they probably get every year.
Google employs more men than women. Even if a higher percentage of women are underpaid, we could see a situation where more men get raises in an equalization.
They were not comparing individuals against some set average and making sure each employee was then brought up to that average and then saying e.g. x% of men and y% of women were bumped up. What they did was compare groups against one another and ensure that there was cross-group consistency. E.g. - the regression formula to determine a man's compensation based on various inputs (such as e.g. performance ratings) should yield a mostly identical result for a women of the same inputs.
And then they contrasted groups against various groups. They found that if you were a women, you were paid disproportionately more than other employees. This is why in their announcement they speak of groups instead of individuals, "[T]he 2018 analysis flagged one particularly large job code (Level 4 Software Engineer) for adjustments. Within this job code, men were flagged for adjustments because they received less discretionary funds than women."
Remember the goal is not to have every individual be identical, but to ensure that there is no specific group bias. Any given man/woman/etc may be paid more than another employee for reasons that cannot be easily quantified, but when the entire group of all e.g. men or all women are disproportionately paid more than other groups of the same quantifiables then you either have inadequate quantifiables (e.g. - you are missing why certain people are paid more), or there is simply no good reason and you have a bias. In this case, they decided it was a bias.
[1] - http://services.google.com/fh/files/blogs/pay_equity_info_sh...
So an average of $908? That sort of rounding error pay adjustment isn't really what people are discussing when the topic of unfair pay comes up.