Making sense of the T32 effect on the NIH funding disparity

July 27, 2021

A new blog post from Mike Lauer, Deputy Director for Extramural Research at the NIH, presents new data on grant award (K and R01equivalent) to applicants assuming they had a prior appointment on a NIH funded T32 training grant. This particular post included some demographic analysis which addressed URM vresus majoritarian status. Leaving aside for a moment the obvious concerns about who gets selected for T32 appointments and where and with whom they are correspondingly training, I was curious about the relative advantage.

Now, the first thing you will recognize is that the critical data are collapsed across all URM groups, although this post doesn’t seem to specify if Asian is included in non-URM, the total N in Table 1 and Table 3 suggest this is the case. This is a little disappointing of course, since the original Ginther report found such striking differences in award rates across URM groups. There was an Asian PI disparity and a Hispanic PI lackthereof, so would it not be super important to keep these groups separate for this post-T32 analysis? Of course it would.

But what got me scratching my head was the presentation of the percentages in Table 3. The show, for example, that 12.9% of non-URM T32 trainees eventually became PI on a funded R01 whereas only 9.8% of URM trainees became PI on a funded R01. That’s all well and good but it obscures the success rate because it doesn’t account for the differential submission rate (24.5% of nonURM T32 trainees eventually submitted an R01, 21.8% of URM trainees).

Doing a little math here, I make it out to be a 52.9% award rate for non-URM applicants and a 45.1% rate for URM applicants. This is a 7.8 percentage point differential which means the URM applicants have a success rate that is 85.3% of the non-URM applicant success rate.

Now we can put this in familiar terms. The Hoppe report found that Black applicants had a success rate that was 60.5% of that enjoyed by white applicants (it was 60.4 in Ginther et al, 2011). So if we take this 24.8 percentage point differential and divide it by the 39.5 percentage point deficit for Black applicants in the Hoppe sample…we end up with about 63% of the Black/white gap reported in Hoppe. The combination of T32 training history, combining of all URM together and adding Asian PIs to the non-URM group closes 63% of the gap.

See my problem here? We want all this broken down so we can answer the most simple question, does T32 training close the gap between Black and white PIs and if so to what extent.

THEN we can go on to ask about other URM populations and the effect of adding the Asian* T32 participants to the non-URM pile.

*reminder that Ginther found that applications with Asian PIs with domestic doctorates had success rates similar to those with white PIs. There is a major complication here with T32 eligibility for Asian-Americans versus Asians who were not T32 eligible as postdocs.

3 Responses to “Making sense of the T32 effect on the NIH funding disparity”

  1. wheresreason Says:

    Has there been an updated Ginther report-type analysis? Ginther was using early 2000’s data, would be good to know how/if things have changed since.
    And yeah, totally weird to bunch all URM’s together.

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  2. drugmonkey Says:

    did you miss Hoppe et al 2019? It replicated the essential findings of Ginther et al 2011.

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  3. wheresreason Says:

    My bad I didn’t elaborate. I was more thinking about recent analysis of funding disparities broken down by URM groups, the Hoppe report specifically looks at African-American/black scientist, and its pretty disappointing nothing has changed.

    Like


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