September 17th, 2017
(written by lawrence krubner, however indented passages are often quotes). You can contact lawrence at: firstname.lastname@example.org
On the surface, this seems to suggest that significant gender discrimination just doesn’t show up in the data. BUT…and this is important…this example highlights the difference between doing math and doing data analysis (or, more charitably, data science)- while this conclusion may be mathematically correct, it’s basically a “garbage in, garbage out” use of econometric tools. Simply put, if you’re trying to isolate gender discrimination, you can’t just blindly control for things that themselves are likely the result of gender discrimination! It’d be like looking at the impact of diet on health and using weight as a control variable- sure, you’d get an “all else being equal” sort of result, but it wouldn’t make sense since weight is likely a step in the chain between diet and health outcomes.
In this way, Google tipped its hand quite a bit regarding the particular nature of gender discrimination at the company- if men and women are paid the same once job title and performance reviews are taken into account, then gender discrimination (if it exists) is taking place either by herding women into jobs with different roles/levels or showing anti-female (or pro-male) bias in performance reviews. (Also, if the “levels” have set pay bands, which the article kind of suggests, doesn’t controlling for level largely amount to assuming your conclusion?)