Even a small difference in the mean or sd for a given attribute will result in enormous gender imbalances for jobs requiring an extreme value of that attribute.
If you select people totally at random (no discrimination!) from the population of people with >130 IQ to work at Facebook and the sd for men in the general population is 11 IQ points instead of 10 for women, you'll wind up with a 70% male workforce at Facebook.
Similarly if men had a mean IQ of 101 instead of 99 for women, Facebook would have a 66% male workforce.
From R:
women <- pnorm(130, mean = 100, sd = 10, lower.tail = FALSE)
men <- pnorm(130, mean = 100, sd = 11, lower.tail = FALSE)
scaling <- 100/(women+men)
men * scaling
[1] 70.28561
women * scaling
[1] 29.71439
women <- pnorm(130, mean = 99, sd = 10, lower.tail = FALSE)
men <- pnorm(130, mean = 101, sd = 10, lower.tail = FALSE)
If you select people totally at random (no discrimination!) from the population of people with >130 IQ to work at Facebook and the sd for men in the general population is 11 IQ points instead of 10 for women, you'll wind up with a 70% male workforce at Facebook.
Similarly if men had a mean IQ of 101 instead of 99 for women, Facebook would have a 66% male workforce.
From R:
women <- pnorm(130, mean = 100, sd = 10, lower.tail = FALSE)
men <- pnorm(130, mean = 100, sd = 11, lower.tail = FALSE)
scaling <- 100/(women+men)
men * scaling
[1] 70.28561
women * scaling
[1] 29.71439
women <- pnorm(130, mean = 99, sd = 10, lower.tail = FALSE)
men <- pnorm(130, mean = 101, sd = 10, lower.tail = FALSE)
scaling <- 100/(women+men)
men * scaling
[1] 65.8503
women * scaling
[1] 34.1497