Another Look at Applications Submitted During the Pandemic (Part 5): A Focus on Career Stage

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In posts from April 2022October 2021June 2021,and July 2020, we looked at the distributions of gender and race of designated principal investigators (PIs) of R01 and RPG applications submitted before and after the onset of the COVID-19 pandemic. Here we extend on our prior analyses by presenting R01-equivalent application data on PI characteristics of Early Stage Investigators (ESIs). As before, we focus on applications received during specific calendar periods.

Tables 1 and 2 show R01-equivalant applications according to career stage. Of note, there was a marked increase in the number of applications received between September 8, 2020, and January 7, 2021, possibly a pandemic effect due to investigators spending more time outside of the physical workspace. The proportion of applications submitted by ESI PIs has been stable.

Table 1: R01-equivalent applications by Career Stage received between May 8 and September 7 in 5 consecutive years

Year Number ESI (%) NI Non-ESI (%) Experienced (%)
2017 12539 14.6 14.7 70.8
2018 12593 14.7 15.6 69.7
2019 13153 17.1 13.6 69.2
2020 13971 16.9 13.2 69.9
2021 13739 17.3 13.8 68.9

Table 2: R01-equivalent applications by Career Stage received between September 8 and January 7 in 5 consecutive years

Year Number ESI (%) NI Non-ESI (%) Experienced (%)
2017/2018 13437 17.5 15.4 67.1
2018/2019 13578 18.2 14.4 67.4
2019/2020 13386 17.3 14.2 68.5
2020/2021 14660 16.6 14.3 69.1
2021/2022 12986 17.3 13.2 69.5

The next set of tables focus solely on applications submitted by ESI PIs. Tables 3 and 4 show increasing proportions of all-women applications. Tables 5 and 6 show increasing proportions of all under-represented-minority (URM) applications. URM includes Black, American Indian, and Pacific Islander Race or Hispanic Ethnicity. Non-URM includes White or Asian Race and Non-Hispanic Ethnicity. For “URM Only,” all PIs had to be an URM. For “Non-URM Only,’ all PIs had to be a Non-URM. All other combinations including those with unknown/withheld race or ethnicity are considered as “Other.”

Table 3: Type 1 R01-equivalent ESI applications by gender received between May 8 and September 7 in 5 consecutive years

Year Number All Men (%) All Women (%) Both (%) Unknown (%)
2017 1828 57.7 36.3 2.6 3.5
2018 1850 55.8 39.5 2.1 2.7
2019 2248 55.4 39.7 1.5 3.4
2020 2364 52.8 41.7 1.7 3.8
2021 2372 50.5 43.7 1.8 4.0

Table 4: Type 1 R01-equivalent ESI applications by gender received between September 8 and January 7 in 5 consecutive years

Year Number All Men (%) All Women (%) Both (%) Unknown (%)
2017/2018 2349 55.9 39.0 2.0 3.1
2018/2019 2466 57.3 37.7 1.3 3.7
2019/2020 2320 54.1 41.2 1.6 3.1
2020/2021 2431 52.5 41.6 1.6 4.2
2021/2022 2251 49.2 43.4 2.3 5.0

Table 5: Type 1 R01-equivalent ESI applications by URM received between May 8 and September 7 in 5 consecutive years

Year Number All URM (%) All Not URM (%) Other (%)
2017 1828 7.7 81.7 10.6
2018 1850 7.9 79.6 12.4
2019 2248 8.2 80.5 11.3
2020 2364 8.3 79.7 12.0
2021 2372 9.5 78.7 11.8

Table 6: Type 1 R01-equivalent ESI applications by URM received between September 8 and January 7 in 5 consecutive years

Year Number All URM (%) All Not URM (%) Other (%)
2017/2018 2349 7.5 81.5 11.0
2018/2019 2466 8.5 79.5 12.0
2019/2020 2320 8.5 80.0 11.5
2020/2021 2431 7.8 80.5 11.7
2021/2022 2251 9.3 77.8 12.9

In summary, we are seeing slowly increasing trends in the proportions of ESI applications submitted by women and by URMs. We will continue monitoring application and funding data over time.

I am grateful to colleagues in the OER Division of Statistical Analysis and Reporting (DSAR) for help with these analyses.

4 Comments

  1. I think the first problem here is the discrepancy between the number of grants awarded to ESI and established PI. I remember another analyses done by NIH showing ~60% grants/ per year goes to PI who has already grant. There should be a mechanism putting limit of awarded grant per PI so that funding can increased for ESI and URM.

    1. I agree. Some labs have five R01s simultaneously, but lots of labs even don’t have one. The sad truth is that PIs with multiple R01s don’t necessarily have better performance in science than their peers who don’t have an R01. The story of Dr. Katalin Karikó, an important contributor to mRNA vaccines, is an excellent example.

  2. I think you need to look at funding rates for second RO1’s. Or how long it takes from first to second. I know a lot of people who fall out here. All of a sudden you have to have 2-3 grants and projects to cover 70% of your salary and most can’t so go to hard money with teaching and admin…

  3. I think this if you really want to look at the effects on the pandemic on grant seeking, you need to take a look at PIs with significant caretaking responsibilities vs those without. This is where you will find the differences. I know you do not have this information readily available, and it would require surveys or some other effort that is probably beyond what you want to do. Nonetheless, just trying to make the point that I think this kind of number crunching where all ESIs or all URMs are grouped together hide the real factors that affected pandemic productivity.

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