The strategy you describe drops the bar for all employees in order to achieve a gender target.
This is not how companies operate, for the most part. If a company wants to hire 1,000 people and 10,000 applicants are in the top 1%, they will move the bar up to hire the top 0.1% instead.
Given a normal distirbution of applicants, there is a huge difference in talent (10x?) between top 1% and top 0.1%. The bar always automatically adjusts higher; otherwise, a competitor will hire the fraction of the 0.1% that you've passed over. Now the competitor has a 1,000 workers, and you have 1,000 workers, but the competitors are 10x more talented for the same pay (most of the 0.1% didn't get an offer from you, so there's no bidding war for their talents).
Actually, if a company spends more money recruiting each equivalent female employee than male employee, they do effectively drop their total hiring bar if spending more money lets the company climb the bell curve, because it's effectively a reduction in spending efficency, but that effect is small, and skill parity is still achieved.
You're assuming you can continuously & linearly rank every single applicant. The labor market doesn't work that way. Typically, it's organized into tiers - you have your superstars, and then you have a pool of developers that are good enough, and then you have a bunch of clueless n00bs. Within a tier, it's rare to find significant, measurable performance differences. The studies showed a 10x difference in productivity between the best teams and the worst teams - that does not mean it applies to individuals, or that it means the best developer is 2x as good as the second best developer, at least on an industry-wide level.
(How would you stack-rank John Resig against Rob Pike? The two of them against Zed Shaw? The three of them against Guido van Rossum? Note also that even if you can stack rank their accomplishments, that won't necessarily reflect in their day-to-day performance. Guido van Rossum wrote Python, but he also wrote a bunch of AppEngine code that isn't all that much beloved.)
Even if the population is divided into discrete tiers, the process is still rife with unfairness no matter how you dice it.
If you have 1,000 slots, 10,000 candidates in your tier, and 1,000 of them are women, you can hire any ratio of women to men that you like and all will be equally talented. Great, right? It's great for those that are hired; not so much for everyone else.
Say you make the gender ratio 50%. You hire 500 women and 500 men from the top tier. Every first-class company like Google or Facebook adopts this strategy. This means that the odds of being hired at a first-class company is 50% for women in the top tier, and only 5% for men in the top tier. For every interview a woman does, a man must do ten. Eventually all the slots in all first-class companies are filled up, leaving some top-tier men working for second class companies--but no top tier women are working for second class companies.
Right, but you're going to get this unfairness no matter what criteria you use, gender or otherwise.
Say you leave the gender ratio unspecified and instead decide based upon the interviewer's gut feeling. Then you'll bias the hiring process toward schmoozers with good social skills.
Or you decide based on which college the applicant went to. Then you bias it towards people who were willing to shell out for a prestigious piece of paper.
Or you decide based on whoever responds to your offer first. Then you bias it against people who have lives and better things to do with their time than refreshing their e-mail waiting for a callback.
Really, the only solution is to acknowledge that life's not fair, and people sometimes get things for completely arbitrary reasons. Which is really hard for a lot of people to do - it was hard for me - but you end up being a lot more successful when you don't think too hard about all the folks who get undeserved job offers and promotions and think more about how you can tilt the odds toward being one of the lucky ones instead.
I completely agree. I make this point, though, because the parent article begins,
“You only got that internship because you’re a woman.”
Note that this statement does not imply she is unqualified. She could be absolutely qualified (and probably is). However, in the ficticious tiering example above, the female applicant has 10x higher odds than a male applicant of getting a sought after job at a first-lass company, even though both are equally qualified. For this example, at least, the above statement is explainable (minus the "only" part, which is just mean) by the huge difference in probability between her and her friend. Her friend would have to apply to ten times more internships in order to land an equivalent gig.
This is not how companies operate, for the most part. If a company wants to hire 1,000 people and 10,000 applicants are in the top 1%, they will move the bar up to hire the top 0.1% instead.
Given a normal distirbution of applicants, there is a huge difference in talent (10x?) between top 1% and top 0.1%. The bar always automatically adjusts higher; otherwise, a competitor will hire the fraction of the 0.1% that you've passed over. Now the competitor has a 1,000 workers, and you have 1,000 workers, but the competitors are 10x more talented for the same pay (most of the 0.1% didn't get an offer from you, so there's no bidding war for their talents).
Actually, if a company spends more money recruiting each equivalent female employee than male employee, they do effectively drop their total hiring bar if spending more money lets the company climb the bell curve, because it's effectively a reduction in spending efficency, but that effect is small, and skill parity is still achieved.