Aug 30

Evidence and conclusions

I think this should be the last note on this topic for awhile, but since it’s topical a new piece of data popped up related to possible sources of gender outcome differences in STEM-related fields.


The new piece of data was reported in the NY Time Upshot section, titled “Evidence of a Toxic Environment for Women in Economics”


The core finding is that on an anonymous forum used by economists (grads, post-doc, profs) there are a lot of negatively gendered associations for posts in which the author makes explicitly gendered reference.  In general, posts about men are more professional and posts about women are not (body-based, sex-related and generally sexist).  Note that “more” here is done as a likelihood ratio, mathematically defined but the effect size is not trivially extracted. Because we always like to review primary sources, I dug up the source, which has a few curious features.

First, it’s an undergraduate thesis, not yet peer-reviewed, which is an unusual source.  However, I looked through it and it is a sophisticated analysis that looks to be done in a careful, appropriate and accurate way (mainly based on logistic regression).  I read through it a bit and the method looks strong, but is complex enough that there could be an error hiding in some of the key definitions and terms.

Paper link:


Of note, the paper seems to be a careful mathematical analysis of something “everybody knows” which is that anonymous forums frequently include high rates of stereotype bias against women and minorities.  But be very careful with results that confirm what you already know, that’s how confirmation bias works.  In addition, economists may not be an effective proxy for all STEM fields.  I don’t know of a similar analysis for Psychology, for example.

But as an exercise, lets consider the possibility that the analysis is done correctly and truly captures the fact that there are some people in economics who treat women significantly differently than they treat men, i.e., that their implicit bias affects the working environment.  So we have 3 data points to consider.

  1. There are fewer women in STEM fields than men
  2. There are biological differences between men and women (like height)
  3. There are environmental differences that may affect women’s ability to work in some STEM fields

The goal, as a pure-minded scientist is to understand the cause of (1).  Why are there fewer women in STEM?  The far-too-frequent inference error is when people (like David Brooks) take (2) as evidence that (1) is caused by biological differences.  That’s simply an incorrect inference.

It turns out to be helpful to know about (3) but only because it should reduce you to less certainty that (2) implies (1).  It’s still critical to realize that we do not know that environment causes (1) either.  All we know is that we have multiple hypotheses consistent with the data and we don’t know.

What we do know is that (3) is objectively bad socially.  Even if (2) meant there were either mean or distributional differences between men and women, the normal distribution means there are still women on the upper tail and if (3) keeps them out of STEM, that hurts everybody.

The googler’s memo assumes (2) and reinforces (3), which is clearly and objectively a fireable offense.


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