“Merit” and the NIH disparity of grant award to Black PIs

I was just noticing something that I hadn’t really focused on before in the Hoppe et al 2019 report on the success of grant applications based on topic choices. This is on me because I’d done an entire blog post on a similar feature of this situation back when Ginther et al 2011 emerged. The earlier blog post focused on the quite well established reality that almost all apps are funded up to a payline (or virtual payline for ICs that claim, disingenuously, that they don’t have one) and that the odds of being funded as one moves away from (worse scoring) that payline, the lower the odds. Supplemental Figure S1 in Ginther showed that these general trends were true for all racial groups.

My blog post was essentially focused on the idea that some apps from African-American PIs were not being funded at a given near-miss score while some apps from white PIs were being funded at worse scores.

It’s worth taking a look at this in Hoppe et al. because it is a more recent dataset from applications scored using the new nine point scale.

I was alerted to Table 1 of Hoppe et al. which shows the percentage of the total funded pool of applications from Black and white PIs by the voted percentile rank, binned into 5 percentile ranges (0-4 is good, 85-89%ile bad).

As you would expect, almost all applications in the top two bins (0-9%ile) were funded regardless of PI race. And the chances of an app being funded at a given percentile bin decrease the further they are away from the very top scores. Where it gets interesting is after the 34%ile mark where no Black PI apps were funded. In any score bin. And there was at least one application in each bin save for 65-69, 75-79 and 80-84 which are not worth talking about anyway.

The pinch is observing that at least some applications of white PIs were funded from 35-59th percentile. I.e., at scores that are worse than the score of any app funded with a Black PI. On Twitter I originally screwed up the count because I stupidly applied the bin percentages to the entire population of funded awards. Not so. In fact I need to calculate it per bin.

Now if my current thinking is right, and it may not be, those bonus bins for white PIs represent 25% of the distribution (5 bins, 5%ile points per bin). The supplement Table S1 tells us there were 103,620 applications submitted by white PIs so that leaves us with 25,905 applications, 5,181 in each bin.

This is very rough.

Percentiling of applications is within a rolling three rounds of each standing study section. Special Emphasis Panels are variously percentiled- sometimes against an associated parent study section, sometimes against the total CSR pool.

But let’s take this as the aggregate for discussion.

Multiplying each of the bin success rates, I end up with a total of 119 applications of white PIs funded from 34-59th percentile. A score range at which ZERO applications were funded to Black PIs.

So, in essence, you could replace all of those applications funded to white PIs with more meritorious (well? that’s how they use the rankings. percentile = merit) unfunded applications submitted by Black PIs. Even by some distance as only 74% of 10-14%ile scoring applications with Black PIs were funded for example.

I was curious why Hoppe et al included the Table and what use they made of it. I could find only one mention of Table 1 and it was in the section titled “IC decisions do not contribute to funding gap“.

However, below the 15th percentile, there was no difference in the average rate at which ICs funded each group (Table 1); applications from AA/B and WH scientists that scored in the 15th to 24th percentile range, which was just above the nominal payline for FY 2011–2015, were funded at similar rates (AA/B 25.2% versus WH 26.6%, P = 0.76; Table 1). The differences we observe at narrower percentile ranges (15 to 19, 20 to 24, 25 to 29, and 30 to 34) slightly favored either AA/B or WH applicants alternately but were in no case statistically significant (P ≥ 0.13 for all ranges). These results suggest that final funding decisions by ICs, whether based on impact scores or discretionary funding decisions, do not contribute to the funding gap.

This is more than a little annoying. Sure, they sliced and diced the analysis down to where it is not statistically resolvable as a difference. But real world? It’s not a matter of constant anger for any PI who has a near miss score and gets wind of anyone being funded at a worse score? Sure it is.

And that last statement is just plain false. 119 white PI applications funded at worse scores is 46.5% of the total number of applications funded with Black PIs. If all of those discretionary funding decisions had gone to Black PIs, that would raise the hit rate from 10.7% to 15.6% for Black PIs. Whereas the white PI hit rate would plunge from 17.7% to…17.56%.

So this analysis they are referring to supports quite the opposite conclusion. Discretionary funding decisions, i.e. outside of percentile ranks where nearly every application is funded, do in fact contribute substantially to the disparity.

and correcting this to give Black PIs a fair hit rate, by selecting applications of HIGHER MERIT, would cause an entirely imperceptible change in the chances for white PIs.