Black PIs working on the “right” topics are at a further NIH funding disadvantage

December 4, 2020

One of the potential takeaway messages from the Hoppe et al 2019 finding, and the Open Mike blogpost, is that if Black PIs want to have a better success rate for their applications, perhaps they should work on the right topics. The “right” topics, meaning the ones that enjoy the highest success rates. After all, the messaging around the release of Hoppe was more or less this: Black PI apps are only discriminated against because they are disproportionately proposed on topics that are discriminated against.

(Never mind that right in the Abstract of Hoppe they admit this only explains some 20% of the funding gap.)

We find, however, a curious counter to this message buried in the Supplement. I mentioned this in a prior post but it bears posting the data for a more memorable impact.

The left side of Figure S6 in Hoppe et al. shows the percent of applications within both the African-American/Black and WHite distributions that were submitted which landed in topic clusters across the success rate quintiles. The 1st is the best, i.e, the most readily funded topic cluster. We can see from this that while applications with white PIs are more or less evenly distributed, the applications with Black PIs are less frequently landing in the best funded topic clusters and more frequently landing in the lowest funded topic clusters. Ok, fine, this is the distributional description that underlies much of the takeaway messaging. On the right side of Figure S6, there is a Table. No idea why they chose that instead of a graph but it has the tendency to obscure a critical point.

Here I have graphed the data, which is the success rate for applications which fall into the topic-success quintiles by the race of the PI. This, first of all, emphasizes the subvocalized admission that even in the least-fundable topic clusters, applications with white PIs enjoyed a success advantage. Actually this main effect was present in each quintile, unsurprisingly. What also immediately pops out, in a way it does not with the table representation, is that in the best funded topic area the advantage of applications with white PIs is the greatest. Another way to represent this is by calculating the degree to which applications with Black PIs are disadvantaged within each quintile.

This represents the success for applications with Black PIs as a percentage of the success for applications with white PIs. As you can see, the biggest hit is at the first and fifth quintiles with the applications faring the best at the middle topic-success quintile. Why? Well one could imagine all kinds of factors having to do with the review of applications in those topic domains. The OpenMike blog post on ICs with lower funding rates (because they have tiny budgets, in part) may explain the fifth quintile but it wouldn’t apply to the top quintile. In fact quite the contrary. Ok, this depiction speaks to the relative hit to success rates within quintile. But the applicant might be a little more interested in the raw percentile hit, especially given the cumulative probability distributions we were discussing yesterday. Recall, the average difference was 7 percentile points (17.7% for Wh PI apps vs 10.7% for Black PI apps).

The disparity is highest in the first quintile. It is a hit of 10.8 percent, as opposed to the all-apps average hit of 7.0 percent.

Obviously we cannot draw much more from the available data. But it certainly cautions us that pushing Black applicants to work on the “right” topics is not a clear solution and may even be counter productive. This is on the acute level of a current PI deciding what to propose in an application, and what to pursue with a multi-application strategy over time. But it also, importantly, serves up a caution for pipeline solutions that try to get more Black trainees into the right labs so that they will work on the right topics using, in Dr. Collins’ parlance, “upgraded” “methodologies”. If this topic/race disparity is not resolved by the time these new trainees hit Assistant Professor stage, we are going to push more Black Professors into research domains that are even harder to succeed in.

We may eventually get some more insight. CSR promised this summer to start looking into study section behavior more closely. It may be that fewer applications from Black PIs in the most successful topic domains is due to disproportionately fewer Black PIs in those fields which leads to fewer of them on the relevant study sections. Even absent that factor, a lower presence in the fields of interest may drive more implicit or explicit bias against the ones that do choose those fields. We just can’t tell without more information about the constitution of study sections and the success rates that emerge from them. Oh, and the exception pay behavior of the Branches and Divisions within each IC. That’s also important to examine as it may relate to topic domain.

5 Responses to “Black PIs working on the “right” topics are at a further NIH funding disadvantage”

  1. Science Geek Says:

    I would not hold my breath on CSR’s outcomes. I take issue with the “right” topic. What is that supposed to mean? And who decides what the “right” topic is? The standing panel? Lauer? Collins? Also, did it ever occur to them that topic choice is reflective of a PI’s overall background and experiences? That these experiences may focus and study aspects of science, health that a group of aging white men living in suburban MD have no clue about? The choices we make as scientists from our doctorate to postdoc and then academia are heavily influenced not only by some charismatic PI (hardly I would say) but life. For once I would like the NIH bean counters to actually look at the numbers and think outside the box and not try to cover their rear ends and self assure they are doing the best they can.


  2. drugmonkey Says:

    This is the part that is hardest to express in simple terms and almost impossible for scientists, convinced firmly of their own belief in the “best and most important science”, to grasp. The degree to which this is a self-reinforcing racket where “the best” is defined by those who have been previously successful. And whether they have been selected by the machine, or beaten into shape by the machine, the effect is an inherent conservatism of the process.


  3. Those of us who have been beaten into shape by the machine are more inclined to see the machine for what it is than those selected by it. The Matthew Effect is very real. Arguing that its inherent conservatism disserves science overall is difficult because there had been enough compensatory intellectual diversity remaining in the funding ecosystem. We are moving towards monoculture, though, to our ultimate adaptive loss.


  4. drugmonkey Says:

    If I take what you are saying correctly, yes, the breadth and diversity of what is funded has a tendency to mask the inherent self-replicating conservatism. This is why I keep on about it. I tend to point to the fact that reviewers are selected from the ranks of the already-successful as my most generalized take on the idea.

    I would be delighted to hear different ways to argue this point with people if you happen to have any good approaches to share.


  5. Yes, you understood what I was saying. The mere fact that someone who already agrees with me in principle has a hard time following me makes me a poor choice for different ways to argue the point with those who may not (know that they) already agree. That’s on me.

    My relative lack of visibility in the more desirable and hence competitive arenas feeds easily into any confirmation bias on this issue. It already does. When I point out systemic issues, my objections are reduced to my own communication problem; they can be and are usually dismissed.

    When it is not bluntly obvious that science would work better, but simply differently, with an arguably fairer and more strategic distribution of funding resources, then inertia wins. I won’t lead the revolution, but I’ll support it when it happens in some other timeline. Meanwhile, I’ll keep getting my funding from private non-profits and trying to give them and the scientific community their money’s worth until I retire in fifteen years and pass the baton to anyone who’s left who cares to pick it up.


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