20 Comments
In posts from October 2021, June 2021,and July 2020, we looked at the distributions of gender, race, and ethnicity of designated principal investigators (PI’s) of R01 and RPG applications submitted before and after the onset of the COVID-19 pandemic. Since that time, we have paid close attention to the well-being of the extramural biomedical research workforce, in part through our survey of institutional leaders and scientists. Others have followed preprint postings, manuscript submissions, and publications, finding evidence of disproportionate effects. Here we add to these earlier analyses and look at NIH R01 and RPG application patterns for calendar dates May 8 through September 7 and September 8 through Jan 7 over the past 5 years.
R01 Equivalent Applications
Table 1 shows the percentage of R01-equivalent applications during May 8 through September 7 from institutions that designated investigators that were either men only, women only, or both men and women (as would be possible for multi-principal investigator applications). The overall number of application submissions has decreased slightly (2%) since 2020 during the pandemic. The proportion of applications from men only has continued to decline, while the proportion of multi-gender applications has increased. The proportion of women-only applications has remained stable. Table 2 shows the same for type 1 (de novo / new) and type 2 (renewal) R01-equivalent applications submitted between September 8 and January 7.
Table 1: R01-equivalent applications by gender received between May 8 and September 7 in 5 consecutive years
| Year | Number | All Men(%) | All Women(%) | Both(%) | Unknown(%) |
|---|---|---|---|---|---|
| 2017 | 12539 | 61 | 25.1 | 12.2 | 1.7 |
| 2018 | 12593 | 59.5 | 26.1 | 12.6 | 1.8 |
| 2019 | 13153 | 59 | 26.3 | 12.7 | 2 |
| 2020 | 13971 | 56.5 | 26.2 | 14.9 | 2.5 |
| 2021 | 13739 | 54.2 | 27 | 16.4 | 2.5 |
Table 2: R01-equivalent applications by gender received between September 8 and January 7 in 5 consecutive years
| Year | Number | All Men(%) | All Women(%) | Both(%) | Unknown(%) |
|---|---|---|---|---|---|
| 2017/2018 | 13437 | 60.2 | 25.9 | 12.3 | 1.5 |
| 2018/2019 | 13578 | 59.6 | 25.8 | 12.8 | 1.8 |
| 2019/2020 | 13386 | 57.8 | 26.6 | 13.7 | 1.9 |
| 2020/2021 | 14660 | 55.7 | 26.1 | 15.6 | 2.6 |
| 2021/2022 | 12986 | 54.7 | 26.7 | 16 | 2.7 |
Table 3 shows the data for self-disclosed race of investigators designated on applications submitted between May 8 and September 7. The proportion of applications coming from Whites only has decreased slightly, while the proportion of mixed applications has increased. “Mixed applications” refers to those applications with multiple investigators with different races identified. Only a small proportion of applications come from under-represented minorities only (including Black/African Americans, Pacific Islanders, and American Indians/Alaska Natives). The same pattern is also seen for applications submitted between September 8 and January 7 as shown in Table 4
Table 3: R01-equivalent applications by race received between May 8 and September 7 in 5 consecutive years
| Year | Number | White Only(%) | Asian Only(%) | Mixed(%) | More Than One(%)* | Unknown Only(%) | URM Only(%) |
|---|---|---|---|---|---|---|---|
| 2017 | 12539 | 56.3 | 24 | 11.7 | 0.9 | 5.4 | 1.6 |
| 2018 | 12593 | 54.8 | 23.8 | 12.8 | 1.1 | 5.6 | 1.7 |
| 2019 | 13153 | 54 | 24.3 | 12.9 | 0.9 | 6 | 1.8 |
| 2020 | 13971 | 51.8 | 24.6 | 15.1 | 0.9 | 5.5 | 2.1 |
| 2021 | 13739 | 50.8 | 23.7 | 16.4 | 0.9 | 6 | 2.2 |
* Application contains only More Than One Race investigators
Table 4: R01-equivalent applications by race received between September 8 and January 7 in 5 consecutive years
| Year | Number | White Only(%) | Asian Only(%) | Mixed(%) | More Than One(%)* | Unknown Only(%) | URM Only(%) |
|---|---|---|---|---|---|---|---|
| 2017/2018 | 13437 | 55.2 | 24.4 | 12.2 | 0.9 | 5.6 | 1.6 |
| 2018/2019 | 13578 | 54.6 | 24.2 | 12.8 | 1 | 5.6 | 1.7 |
| 2019/2020 | 13386 | 53.3 | 24.3 | 14 | 1 | 5.5 | 2 |
| 2020/2021 | 14660 | 50.9 | 24.4 | 15.7 | 0.9 | 6.2 | 1.9 |
| 2021/2022 | 12986 | 49.2 | 25.1 | 16.6 | 0.9 | 6.2 | 2 |
* Application contains only More Than One Race investigators
Research Project Grant (RPG) Applications
Tables 5, 6, 7, and 8 show corresponding data for RPG applications. Similar findings were observed for RPG applications as seen for R01-equivalents.
Table 5: Research Project Grant (RPG) applications by gender received between May 8 and September 7 in 5 consecutive years
| Year | Number | All Men(%) | All Women(%) | Both(%) | Unknown(%) |
|---|---|---|---|---|---|
| 2017 | 19773 | 59.4 | 26.7 | 11.2 | 2.7 |
| 2018 | 19419 | 58 | 27.6 | 11.6 | 2.7 |
| 2019 | 19201 | 57.5 | 27.5 | 12.2 | 2.7 |
| 2020 | 22554 | 56.4 | 26.8 | 13.3 | 3.6 |
| 2021 | 20429 | 53.9 | 27.8 | 14.9 | 3.3 |
Table 6: Research Project Grant (RPG) applications by gender received between September 8 and January 7 in 5 consecutive years
| Year | Number | All Men(%) | All Women(%) | Both(%) | Unknown(%) |
|---|---|---|---|---|---|
| 2017/2018 | 20611 | 57.9 | 28 | 11.5 | 2.6 |
| 2018/2019 | 20496 | 57.7 | 27.5 | 12.1 | 2.7 |
| 2019/2020 | 20884 | 56.2 | 27.7 | 12.9 | 3.1 |
| 2020/2021 | 22244 | 54.5 | 27 | 14.8 | 3.7 |
| 2021/2022 | 19453 | 53.8 | 27.5 | 15.1 | 3.7 |
Table 7: Research Project Grant (RPG) applications by race received between May 8 and September 7 in 5 consecutive years
| Year | Number | White Only(%) | Asian Only(%) | Mixed(%) | More Than One(%)* | Unknown Only(%) | URM Only(%) |
|---|---|---|---|---|---|---|---|
| 2017 | 19773 | 55.5 | 23.7 | 11 | 1 | 6.7 | 2 |
| 2018 | 19419 | 54.5 | 23.7 | 11.8 | 1.2 | 6.7 | 2.2 |
| 2019 | 19201 | 53.8 | 24 | 12.3 | 1 | 6.9 | 2 |
| 2020 | 22554 | 51.3 | 24.9 | 13.6 | 1 | 6.8 | 2.4 |
| 2021 | 20429 | 50.5 | 24.2 | 15 | 1 | 6.8 | 2.4 |
* Application contains only More Than One Race investigators
Table 8: Research Project Grant (RPG) applications by race received between September 8 and January 7 in 5 consecutive years
| Year | Number | White Only(%) | Asian Only(%) | Mixed(%) | More Than One(%)* | Unknown Only(%) | URM Only(%) |
|---|---|---|---|---|---|---|---|
| 2017/2018 | 20611 | 54.5 | 24.3 | 11.2 | 1 | 6.8 | 2.1 |
| 2018/2019 | 20496 | 54 | 24 | 12.2 | 1 | 6.8 | 2 |
| 2019/2020 | 20884 | 52.4 | 24.2 | 13.1 | 1.1 | 6.9 | 2.2 |
| 2020/2021 | 22244 | 49.9 | 24.6 | 14.9 | 1 | 7.4 | 2.2 |
| 2021/2022 | 19453 | 49.1 | 25 | 15.5 | 1 | 7.2 | 2.2 |
* Application contains only More Than One Race investigators
Summary
These data suggest there are no particularly marked changes in the high-level demographics of designated PI’s on R01-equivalent and RPG applications during the course of the pandemic. 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 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.
I am grateful to my colleagues in the NIH Office of Extramural Research (OER) Division of Biomedical Research Workforce and Office of Research Reporting and Analysis, Division of Statistical Analysis and Reporting for their help collecting these data and conducting these analyses.
Is there a reason why there is no breakdown of the other ethnicities?
Thank you for the comment. We focused on the race of investigators here , but we’ll consider adding ethnicity for future analyses.
The trends for R15s would also be of interest… has COVID had a bearing on the productivity of different types of institutions; the R15 being the best way to show that?
How is it that you can track race and ethnicity? Are investigators forced to describe themselves as being of a particular race? How is this verified? Is “prefer not to say” an option? All of this is decidedly non-objective and unscientific and a waste of resources that could be going to real scientific investigations.
How are gender non-conforming/non-binary individuals represented?
Thank you for the comment. The information is based on data that is voluntarily self-reported in the eRA Commons profile. Options provided are female, male, and do not wish to provide. NIH continues discussions with experts to consider methods of capturing gender identity, including a recently-released NIH-funded NASEM report on this topic: https://www.nationalacademies.org/our-work/measuring-sex-gender-identity-and-sexual-orientation-for-the-national-institutes-of-health
I second Jill Shannon’s comment: R15 data would be particularly interesting (given the impact of the pandemic on teaching).
Meyers et al 2020 work suggests that those with young dependents have been particularly affected. Information and monitoring on early career scientists and those with young dependents would be appreciated.
Myers, K.R., Tham, W.Y., Yin, Y. et al. Unequal effects of the COVID-19 pandemic on scientists. Nat Hum Behav 4, 880–883 (2020).
“These data suggest there are no particularly marked changes in the high-level demographics of designated PI’s on R01-equivalent and RPG applications during the course of the pandemic.” What statistics were used to draw this conclusion? There are certainly some notable trends in decreasing percentages of whites and males, ~5% in a 5 year period seems substantial.
It would be interesting to see the breakdown by age or age/gender. Impacts on those of prime child-rearing age are potentially masked by those who are older and younger, and childcare/virtual school responsibilities are more likely to impact women than men at the population level given current societal norms.
What are the breakdowns of new vs. renewal applications, and projects that are closed without renewal vs. those that are submitted for potential renewal? Also -breakdowns by seniority (assistant professor, associate professor, etc.)?
It might be enlightening to know what proportion of the applications in each category was funded.
I second that comment/question
Why gender and race are even a factor in research grant programs is as backwards as its unfortunate.
But global advanced research organizations will have no problem with taking on any passed over applicants with brilliant ideas.
Similar to our procurement of geniuses in the 1930’s; those passed on by their homeland we’re welcomed to America and their landmark projects changed everything.
Bottom line, please keep political red tape out of our young researchers paths. Regardless of identity; we need the best of the best, whoever that may be.
A cutting edge researcher that’s working on ground breaking concepts is just that.
It would be informative to see the funding data associated with these demographics. What percentage of these applications actually got funded?
Interesting that there’s increases in both gender and mixed race collaboration proposals over the 5 years
Submitting an application is not the same as being funded. It would be helpful to know the proportion of applications funded per category and how many of those were new applications from researchers not previously funded by NIH. Thank you.
Please note that Latinx individuals have an extremely difficult time reporting on race. Many feel force to choose one race that doesn’t fit. We can’t choose indigenous even though we know we have indigenous roots. Some can’t choose Black even though they have black ancestry. Forcing them to choose white defeats the purpose of this and doesn’t help understand if there are any changes or improvements. Thank you.
I would like to also know if there’s a correlation between the applications received from these groups and the number of funded applications. If there was an increase in R01 and R01-equivalent apps submitted by women and URM, was there also a concomitant increase in the number of funded applications in those groups? Also, although there seemed to be fewer males that submitted applications, how many of those males were currently funded by R01 and R01 equivalents at the time of application and how many years were remaining on their funding? Is there any association between the “need” for funding in this measurement of applications submitted by specific groups?
I do not understand why you always compare the groups. I think it is time to treat every person with respect and as equals. I will never be ashamed of been white. As a rsearcher and now with technology that will change the world in the way they think about electricity, I find myself in a position where people do not want to fund a project re I am white. Even if this technology will bring cheap sustainabke electricity to all, no matter where you are. It will be manufactured in various sizes for vehicles, boats, ships, trains, planes, households, farms, factories, mines etc. No need to recharge. No overhead lines required, no maintenance and no fuels. What do the world waiting for????