When NIH uses affirmative action to fix a bias

July 20, 2018

We have just learned that in addition to the bias against black PIs when they try to get research funding (Ginther et al., 2011), Asian-American and African-American K99 applicants are also at a disadvantage. These issues trigger my usual remarks about how NIH has handled observed disparities in the past. In the spirit of pictures being worth more than words we can look up the latest update on success rates for RPG (a laundry list of research grant support mechanisms) broken down by two key factors.
First up is the success rate by the gender of the PI. As you can see very clearly, something changed in 2003. All of a sudden a sustained advantage for men disappeared. Actually two things happened. This disparity was “fixed” and the year after success rates went in the tank for everyone. There are a couple of important observations. The NIH didn’t suddenly fix whatever was going on in study section, I guaranfrickentee it. I guarantee there were not also any magic changes in the pipeline or female PI pool or anything else. I guarantee you that the NIH decided to equalize success rates by heavy handed top-down affirmative action policies in the nature of “make it so” and “fix this”. I do not recall ever seeing anything formal so, hey, I could be way off base. If so, I look forward to any citation of information showing change in the way they do business that coincided directly with the grants submitted for the FY2003 rounds.
The second thing to notice here is that women’s success rates never exceeded that for men. Not for fifteen straight Fiscal Years. This further supports my hypothesis that the bias hasn’t been fixed in some fundamental way. If it had been fixed, this would be random from year to year, correct? Sometimes the women’s rates would sneak above the men’s rates. That never happens. Because of course when we redress a bias, it can only ever just barely reach statistically indistinguishable parity and if god forbid the previously privileged class suffers even the tiniest little bit of disadvantage it is an outrage.
Finally, the fact that success rates went in the tanker in 2004 should remind you that men enjoyed the advantage all during the great NIH doubling! The salad days. Lots of money available and STILL it was being disproportionately sucked up by the advantaged group. You might think that when there is an interval of largesse that systems would be more generous. Good time to slip a little extra to women, underrepresented individuals or the youth, right? Ha.

Which brings me to the fate of first-time investigators versus established investigators. Oh look, the never-funded were instantly brought up to parity in 2007. In this case a few years after the post-doubling success rates went in the toilet but more or less the same pattern. Including the failure of the statistically indistiguishable success rates for the first timers to ever, in 11 straight years of funding, to exceed the rates for established investigators. Because of affirmative action instead of fixing the bias. As you will recall, the head of the NIH at that time made it very clear that he was using “make it so” top-down heavy handed quota based affirmative action to accomplish this goal.

Zerhouni created special awards for young scientists but concluded that wasn’t enough. In 2007, he set a target of funding 1500 new-investigator R01s, based on the previous 5 years’ average.

Some program directors grumbled at first, NIH officials say, but came on board when NIH noticed a change in behavior by peer reviewers. Told about the quotas, study sections began “punishing the young investigators with bad scores,” says Zerhouni.

“quotas”.

I do not recall much in the way of discussing the “pipelines” and how we couldn’t possible do anything to change the bias of study sections until a new, larger and/or better class of female or not-previously-funded investigators could be trained up. The NIH just fixed it. ish. permanently.

For FY2017 there were 16,954 applications with women PIs. 3,186 awards. If you take the ~3% gap from the interval prior to 2003, this means that the NIH is picking up some 508 research project grants from women PIs via their affirmative action process. Per year. If you apply the ~6% deficit enjoyed by first time investigators in the salad days you end up with 586 research project grants picked up by affirmative action. Now there will be some overlap of these populations. Women are PI of about 31% of applications in the data for the first graph and first timers are about 35% for the second. So very roughly women might be 181 of the affirmative action newbie apps and newbies might be 178 of the affirmative action women’s apps. The estimates are close. So let’s say something like 913 unique grants are picked up by the NIH just for these two overt affirmative action purposes. Each and every Fiscal Year.

Because of the fact that, for example, African-American PIs of research grants or K99 apps represent such tiny percentages of the total (2% in both cases), the number of pickups that would be necessary to equalize success rate disparities is tiny. In the K99 analysis, it was a mere 23 applications across a decade. Two per year. I don’t have research grant numbers handy but if we use the data underlying the first graph, this means there were about 1,080 applications with African-American PIs in FY2017. If they hit the 19% success rate this would be about 205 applications. Ginther reported about a 13% success rate deficit, working out to 55% of the success rate enjoyed by white applicants at the time. This would correspond to a 10.5% success rate for black applicants now, or about 113 application. So 92 would be needed to make up the difference for African-American PIs assuming the Ginther disparity still holds. This would be less than one percent of the awards made.

Less than one percent. And keep in mind these are not gifts. These are making up for a screwjob. These are making up for the bias. If any applicants from male, established or white populations go unfunded to redress the bias, they are only losing their unearned advantage. Not being disadvantaged.

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