I am happy to be proven wrong if the data supports it. I am also not ashamed to make speculations which are backed by logic; which is what I have done. If I am wrong then so be it. But I don't think this supports the discrimination hypothesis. In fact I think you are the one with subconscious bias, and I'll explain why. What I essentially state was "hey I have a hunch that this is the way things are, but I am not certain." You, on the other hand, have jumped to a concrete conclusion based off of a single NY times article that happens to align with your preconceived notion. That is the kind of bias that results in discrimination today. You have jumped to a concrete conclusion based off one article. Re-read my post and see how I specifically use language like "probably" and "I'd bet." The difference is I form opinions and hypotheses, but do not jump to premature conclusions that I think are absolute certainties (unlike what you have done). I keep my mind open and moldable, never stating things with 100% certainty unless there is sufficient evidence. You confidently state "Nope. Male nurses still make more..." -- which in my opinion, is premature to conclude based on a single NY times article.

Ok, then how many studies would you need? Kind of seems like you don't want to believe the data when presented with it. To control for the "male nurses take are more likely to be specialized" argument, here's one showing that a specific Nursing profession (CRNA) has a significant gender pay gap: http://www.geeksonfinance.com/info_7850041_average-hourly-sa...

The reason for the gap is of course up for debate, but it's still there.

I want the raw data so I can study it for myself.

A discrimination by women against women then since nursing is overwhelmingly women.

I don't know if that's true, but even if it is, how does that make the discrimination any less of a problem?

If we can't even agree on what kind of discrimination happens then we certainly can't do more than social media inactivism. At best we can make the problem worse by trying to solve the wrong problem.

The kind of discrimination is well-defined: it's women making less than men. Whether the employers choosing the salaries are women or men is interesting information, but it doesn't significantly change the approach to solving the underlying problem.