In conversations about improving diversity in STEM, I tend to run into “well-meaning” faculty who are resolutely against quotas for fear that they will only exacerbate impostor syndrome and other negative perceptions (e.g. “you only got in because you’re black”). This is such a frustrating position and although I haven’t quite found a time, place, or way to push back against it yet, in my deepest heart of hearts I know it’s fundamentally foolish.
First of all: why is it that in issues of diversity, we suddenly become incapable of being quantitative scientists? How are you going to know whether your programs are working if you don’t set targets and have ways to measure your progress? How else can accountability truly happen?
Second of all: why is it that we go immediately from “we don’t want people to think they didn’t get here on their own merit” to “therefore we shouldn’t have quotas”? Why can’t we be more creative about the situation - if the true problem is that we don’t want minority hires to feel unsupported or disrespected, why don’t we actually address that problem instead? Where is the creativity? You could make the admissions/hiring process more transparent (like perhaps with explicit criteria and “suitable substitutes or allowable similarities”), you could publicly acknowledge and value all of the contributions the new hire is making to your department, you could foster a community that acknowledges and deeply values all the lived experiences and perspectives of their different members, and surely so many other things that would benefit not only the new hires but also everyone else.
Third of all: wait but seriously why not? It works.
From this article, deliciously titled “Gender quotas and the crisis of the mediocre man”:
We ask how competence was affected by a “zipper” quota, requiring local parties to alternate males and females on the ballot, implemented by the Social Democratic party in 1993. Far from being at odds with meritocracy, this quota raised the competence of male politicians where it raised female representation the most. We argue that resignations of mediocre male leaders was a key driver of this effect.
We show that competence increased following the introduction of the quota, and more so in municipalities where the quota led to the biggest increase in the proportion of elected women. Contrary to the expectations of quota sceptics, women’s competence did not to go down but stayed roughly constant. However, the competence of the men went up significantly.
“We have shown that a larger bite of the Social Democratic gender quota raised the competence of elected candidates. This higher competence is due to the selection of male politicians, with no significant change of female competence. A careful look at the time patterns among leaders and followers reveals that the competence of male party leaders went up in the first election under the quota, while the competence of elected male followers went up in the next few elections. Upon closer inspection, the immediate improvement in leadership competence reflects a lower than usual survival rate among mediocre leaders.”
Very nice. Not sure how large the effect sizes really are (and the figures are really freaking hard to interpret) but I love this idea and data and framing. It’s no secret that increasing diversity means that some of the people in power have to “let go of their unearned power”. It’s nice to see it tested in a real-world scenario, and show that everybody ends up winning (except the people who used to be winning without really deserving it).
Some side notes purely about the paper:
They use income as a proxy for competence. That is, politicians who (before and after they were politicians) make more money relative to other people with similar demographics are considered competent. Very much dislike the money –> value direct link. Not cool but hey, I guess it’s a good enough proxy given, you know, capitalism.
Omg social science researchers need to get better at communication. This paper should have had three figures and been no more than 6 pages. It has 6 figures, 5 tables, and is 32 pages long. wowowow. What a slog!