More Data on Applications Submitted During the Pandemic


Twice since the onset of the public health emergency we have taken a look at the number of research applications submitted together with some demographic information on the affiliated scientists (see this July 2020 post for data on the May-June timeframe and this June 2021 post for the January to April timeframe). It was interesting, and maybe a bit reassuring, that there were no particularly marked changes seen in the demographics of designated PI’s on R01-equivalent and research project grant (RPG) applications even one year into the pandemic.

We are continuing to follow this analysis again here, focusing on NIH R01-equivalent and RPG application patterns during April 9 and August 8 over the past five years. Data include new (Type 1) and renewal (Type 2) applications, along with the self-disclosed race and gender of the designated principal investigators.

Tables 1 and 2 below show the data for R01-equivalent applications broken down by either the gender or race of the investigator(s), respectively. The vast majority of applications were Type 1s during this cycle, but overall there was a 2 percent decrease in the number of R01-equivalent applications submitted this year compared to last. The percentage of applications from men only has decreased, and from women only has remained relatively stable. The percentage of multi-PI applications with men and women increased. As it relates to race, the proportion of applications with white only investigators decreased, went up for applications where multiple investigators with different races were identified (mixed), and held relatively steady for Asians and underrepresented minorities (URMs). The latter group includes black/African Americans, Pacific Islanders, and American Indians, and though their percentage remained relatively stable, they still made up a small fraction of the larger applicant pool.


Table 1: R01-equivalent applications by gender received between April 9 and August 8 in 5 consecutive years

Year Total Men Women Both Unknown
2017 11,813 60.70% 24.80% 12.80% 1.70%
2018 12,420 58.90% 26.20% 13.00% 1.90%
2019 12,268 58.20% 25.80% 13.90% 2.00%
2020 13,193 55.90% 25.30% 16.40% 2.50%
2021 12,917 54.30% 26.20% 17.10% 2.50%


Table 2: R01-equivalent applications by Race received between April 9 and August 8 in 5 consecutive years

Year Total White Asian Mixed More than One Race Only* Unknown URM
2017 11,813 55.90% 23.60% 12.60% 0.90% 5.40% 1.60%
2018 12,420 54.60% 23.70% 13.10% 1.10% 5.80% 1.70%
2019 12,268 53.40% 23.90% 14.10% 0.80% 6.00% 1.70%
2020 13,193 51.20% 23.90% 16.50% 0.80% 5.70% 1.80%
2021 12,917 51.10% 23.10% 17.10% 0.80% 6.00% 1.90%
  • Application contains only More Than One Race investigators

Now, let’s look at the gender (Table 3) and race (Table 4) of investigators on RPG applications. This year, 8 percent fewer RPG applications were submitted compared to 2020, but 7% more than in 2019. The same general trends for gender and race described above were observed here too.


Table 3: RPG applications by gender received between April 9 and August 8 in 5 consecutive years

Year Total Men Women Both Unknown
2017 18,891 59.00% 26.70% 11.50% 2.80%
2018 19,188 57.50% 27.70% 11.90% 3.00%
2019 18,445 56.50% 27.30% 13.10% 3.20%
2020 21,489 55.60% 26.20% 14.30% 3.90%
2021 19,777 53.90% 27.20% 15.40% 3.50%


Table 4: RPG applications by Race received between April 9 and August 8 in 5 consecutive years

Year Total White Only Asian Only Mixed More Than One Race Only* Unknown/Withheld Only URM
2017 18,891 55.20% 23.40% 11.50% 1.00% 6.90% 2.00%
2018 19,188 54.20% 23.40% 12.00% 1.20% 7.00% 2.20%
2019 18,445 53.10% 23.60% 13.20% 0.90% 7.30% 1.90%
2020 21,489 50.70% 24.40% 14.60% 0.90% 7.20% 2.20%
2021 19,777 50.70% 23.70% 15.40% 1.00% 7.20% 2.20%
  • Application contains only More Than One Race investigators

We will continue to monitor these application counts and other factors going forward to get a more complete picture of the effects of the pandemic on our supported research activities and workforce. We also recognize and appreciate that the effects may not be equally felt across all institutions, investigators, and research areas. NIH continues to offer various administrative flexibilities to help, and we encourage you reach out to your institutional staff as well as our program and grants management staff for additional information.


  1. Appreciate NIH is collecting and sharing these data. These findings continue to concern me. Beyond submissions, I would like to see the race and gender breakdown by grants as well in this table, so an apples to apples comparison can be made.

  2. Why is race delineated that way? Where are Black and Hispanic? What is NIH doing to improve equity for Black and Hispanic researchers?

    1. Agreed Dr Spelman.

      Addition of Black, African American, Hispanic and Latinx, and cross sectional analysis across all categories inclusive of gender, as well as detailed information among those who report as mixed would be useful. The number of Black or African American applicants is extremely low, and may be embedded within “unknown” or “URM” categories.

  3. I would like to see a breakdown on age, and if possible age of children. I think the pandemic has hit those with children, particularly young children, much harder. You wouldn’t see that here. I don’t think parenthood or anything is collected, but maybe a survey could be used to collect it. Also, other FMLA related activities–for instance, I would guess those caring for elderly parents were also hit hard during the pandemic.

  4. Maurice’s comment is critical. I hope that you can implement it, even though “race” is a moving target, particularly as Hispanic is “White”. But we can do this.
    Marianne Berwick

    1. Hispanic includes many races, as it is a regional and cultural classification. Are Indigenous peoples included in the data collection? Kudos to NIH for reporting. It would be interesting to see this level of detail across all funding resources offered throughout the federal government and all public-private partnerships.

  5. Could you run the analysis for SBIRs? The NIH has been marketing the program heavily over the last several years, together with the STTR program. It would be interesting to see the data over 5 years, and also the mix of applicants.

  6. It took a bit of re-reading to find that URM = Underrepresented Minority, a combination of Black/African-American, Native American, and Other Pacific Islander. While it’s not shocking that this COMBINED percentage is so low, it is heartbreaking. At least it went up in the last five years.
    Please add Ethnicity (Hispanic/Latino or not) to your data, as well as disability status (or at least explain why they aren’t included).
    Thank you for tracking the data.

  7. Thank you for sharing these data. I would love to see a breakdown of ESIs and New Investigator submissions vs. established investigator (further categorized by sex and race/ethnicity). ESIs are more likely to have small kids and were severely affected by the pandemic.

  8. Thanks for collecting and sharing this data. Please be aware that the effects of the pandemic shutdowns will likely be reflected in productivity (grants and publications) in the 2-3 year period AFTER the shutdown. Grants submitted in summer 2020 are those with preliminary data collected in prior years; actually, the fact that we were working from home gave many PIs the chance to write grants with data already collected. Covid restrictions impinged on collection on new data which, I believe, will be reflected more in 2022, 2023, 2024 grant submission cycles, particularly for ESI and new investigators.

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