Mentored Career Development Application (K) Funding Rates by Race-Ethnicity FY 2010-FY 2022

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Photo of Marie A. Bernard, M.D.
Marie A. Bernard, M.D., Chief Officer for
Scientific Workforce Diversity

In this post, we analyze funding rates for Fiscal Year (FY) 2010 to FY 2022 mentored career development award (K) applicants according to the race-ethnicity of designated Principal Investigators (PIs). Receipt of a career development award often presages R01 receipt. We used data from frozen, official, NIH success rate files. We focus on applicants for K01, K08, K23, K25,and K99 direct budget authority awards.

We obtained data on the race and ethnicity of PIs from their entries into the eRA Commons Personal Profile. As noted by NIH, PIs provide these data on a strictly voluntary basis, and these data are not used for making funding decisions. If individuals described themselves as Hispanic in the ethnicity field and not Black in the race field, then race-ethnicity was considered to be Hispanic; otherwise the individual’s race-ethnicity was based on their entry in the race field. Race-ethnicity groups with small cell sizes are removed from the report.

As a reminder, NIH publishes three main kinds of grant outcome metrics, namely award rates, success rates, and funding rates. Award and success rates are application-based metrics, while funding rates are person-based. Throughout the report we refer for convenience to Principal Investigators (“PIs”) as applicants or awardees. In point of fact, applicants and awardees are institutions who in turn designate PIs; PIs are typically employees of the applicants and awardees.

Tables 1 and 2 show characteristics according to race-ethnicity of scientists who were designated as a PI on at least one K application submitted in FY 2012 (the year the Ginther paper was published) and in FY 2022. Compared to white applicants, FY 2022 Black applicants were more likely to submit a K01 application and less likely to submit K08 or K23 applications. The K01 mechanism is used by some ICs to enhance workforce diversity. Black applicants were more likely to submit proposals including human subjects and less likely to submit proposals including animal models.

Table 1: Characteristics according to race-ethnicity of scientists who were designated as a Principal Investigator on at least one K application in FY2012. ND = not displayed due to small cell size.

Characteristic White Asian Unknown Hispanic Black
Total N (%) 1255 (52.0) 587 (24.3) 280 (11.6) 153 (6.3) 94 (3.9)
Female 583 (46.5) 251 (42.8) 119 (42.5) 84 (54.9) 71 (75.5)
Age (years) Median (IQR) 36.0 (33.0 to 39.0) 37.0 (34.0 to 40.0) 35.0 (33.0 to 38.0) 37.0 (35.0 to 40.0) 36.5 (34.0 to 40.0)
Degree MD 294 (23.4) 145 (24.7) 37 (13.2) 36 (23.5) 24 (25.5)
MD-PhD 128 (10.2) 82 (14.0) 22 (7.9) 19 (12.4) ND
Other 29 (2.3) 19 (3.2) 91 (32.5) ND ND
PhD 804 (64.1) 341 (58.1) 130 (46.4) 93 (60.8) 65 (69.1)
Submitted a K01 application 277 (22.1) 94 (16.0) 41 (14.6) 51 (33.3) 47 (50.0)
Submitted a K08 application 198 (15.8) 101 (17.2) 36 (12.9) 19 (12.4) ND
Submitted a K23 application 302 (24.1) 116 (19.8) 56 (20.0) 40 (26.1) 23 (24.5)
Submitted a K25 application 37 (2.9) 24 (4.1) ND ND ND
Submitted a K99 application 444 (35.4) 252 (42.9) 138 (49.3) 41 (26.8) 15 (16.0)
Submitted a K animal research application 499 (39.8) 298 (50.8) 141 (50.4) 71 (46.4) 27 (28.7)
Submitted a K human research application 675 (53.8) 245 (41.7) 119 (42.5) 77 (50.3) 60 (63.8)

Table 2: Characteristics according to race-ethnicity of scientists who were designated as a Principal Investigator on at least one K application in FY2022. ND = not displayed due to small cell size.

Characteristic White Asian Unknown Hispanic Black
Total N (%) 1859 (51.2) 907 (25.0) 296 (8.2) 284 (7.8) 195 (5.4)
Female 1045 (56.2) 419 (46.2) 77 (26.0) 169 (59.5) 133 (68.2)
Age (years) Median (IQR) 36.0 (34.0 to 39.0) 36.0 (34.0 to 39.0) 36.0 (34.0 to 39.0) 37.0 (34.0 to 40.0) 38.0 (35.0 to 42.0)
Degree MD 351 (18.9) 176 (19.4) 49 (16.6) 41 (14.4) 43 (22.1)
MD-PhD 182 (9.8) 105 (11.6) 29 (9.8) 18 (6.3) 14 (7.2)
Other 179 (9.6) 93 (10.3) 95 (32.1) 27 (9.5) 17 (8.7)
PhD 1147 (61.7) 533 (58.8) 123 (41.6) 198 (69.7) 121 (62.1)
Submitted a K01 application 370 (19.9) 115 (12.7) 43 (14.5) 66 (23.2) 73 (37.4)
Submitted a K08 application 314 (16.9) 138 (15.2) 62 (20.9) 35 (12.3) 20 (10.3)
Submitted a K23 application 463 (24.9) 163 (18.0) 56 (18.9) 49 (17.3) 47 (24.1)
Submitted a K25 application 23 (1.2) 13 (1.4) ND ND ND
Submitted a K99 application 692 (37.2) 481 (53.0) 133 (44.9) 133 (46.8) 54 (27.7)
Submitted a K animal research application 594 (32.0) 402 (44.3) 118 (39.9) 115 (40.5) 48 (24.6)
Submitted a K human research application 1121 (60.3) 415 (45.8) 147 (49.7) 149 (52.5) 137 (70.3)
Submitted a MOSAIC research application 16 (0.9) ND ND 38 (13.4) 24 (12.3)

Figure 1 shows the number of unique K applicants each fiscal year according to race-ethnicity.

Figure 1: Number of K applicants according to race-ethnicity by fiscal year. Panel A shows data for all groups, while Panel B shows the same data but for White, Hispanic, and Black applicants only.

Figure 1 comprises two graphs, A and B. Graph A is a line graph showing the number of K applicants according to race-ethnicity by fiscal year. On the X axis are Fiscal Years from 2010 to 2022. On the Y axis are number of K applicants from 0 to 1900. White applicants are plotted in orange circles, Asian applicants in yellow triangles, Hispanic applicants in blue bars, Black applicants in purple bars with crosshatch boxes, and Unknown applicants in green squares. Figure 2 shows the same data, but with only White applicants, Hispanic applicants, and Black applicants.

Figure 2 shows increasing numbers of unique Black K applicants and awardees, while Figure 3 shows corresponding values for Hispanic applicants and awardees.

Figure 2: Number of unique mentored K Black applicants and awardees by fiscal year

Figure 2 is a stacked bar graph showing the number of unique mentored K Black applicants and awardees by fiscal year. On the X axis are Fiscal Years from 2010 to 2022. On the Y axis are the number of applicants going from 0 to 200. Funded applicants are shown in teal and Unfunded are shown in orange.

Figure 3: Number of unique mentored K Hispanic applicants and awardees by fiscal year

Figure 3 is a stacked bar graph showing the number of unique mentored K Hispanic applicants and awardees by fiscal year. On the X axis are Fiscal Years from 2010 to 2022. On the Y axis are the number of applicants going from 0 to 300. Funded applicants are shown in teal and Unfunded are shown in orange.

Figure 4 shows K funding rates according to race-ethnicity.

Figure 4: Funding rates for K applicants according to race-ethnicity by fiscal year. Panel A shows data for all groups, while Panel B shows the same data but for White, Hispanic, and Black applicants only.

Figure 4 comprises two graphs, A and B. Graph A is a line graph showing the funding rate of K applicants according to race-ethnicity by fiscal year. On the X axis are Fiscal Years from 2010 to 2022. On the Y axis is the funding rate from 0 to 45. White applicants are plotted in orange circles, Asian applicants in yellow triangles, Hispanic applicants in blue bars, Black applicants in purple bars with crosshatch boxes, and Unknown applicants in green squares. Figure 2 shows the same data, but with only White applicants, Hispanic applicants, and Black applicants.

Of note, in FY 2022 there were 195 Black applicants; of these 54 were K99 applicants, including 24 who were MOSAIC applicants. There were 88 Black awardees; of these 28 were K99 awardees, including 16 who were MOSAIC awardees. There were 284 Hispanic applicants; of these 133 were K99 applicants, including 38 who were MOSAIC applicants. There were 104 Hispanic awardees; of these 46 were K99 awardees, including 18 who were MOSAIC awardees.

In summary, in this analysis of K applicants and awardees, we find that:

  • The numbers of Black and Hispanic applicants and awardees have steadily increased over time.
  • Funding rates for Black applicants have increased over the past 3 years.
  • However, the total number of Black and Hispanic applicants remains quite low.

We are grateful to our colleagues in the NIH Office of Extramural Research Division of Statistical Analysis and Reporting (DSAR) for their help with these analyses. For a more in-depth version of this report, please see the full report.

Editorial note added on October 16, 2023: Native Hawaiian or Other Pacific Islanders as well as American Indian or Alaska Native researchers were excluded from the report as noted in the blog and full length reports (linked at the end) due to small cell sizes. We do not publicly report sample sizes that are sufficiently small (<12), which is considered as potentially identifiable. Separately, our intent was to focus on African American/Black and Hispanic applicants in some of the panels here, but we will only show one panel (that includes Asian and other researchers) going forward on any future analyses.

15 Comments

  1. In the target analysis, why is there no mention of the ultra low rate by asians who are from a even smaller minority group?

  2. What is the intent of removing Asians in Figure 4B?
    Is your data showing that Asian applicants have the lowest funding rate?

  3. A reader also reached out via email asking if the “28 Black K99 awardees is comprised of 16 MOSAIC awards and 12 original mechanism K99 awardees? Do these numbers include applications submitted in 2021 but awarded in 2022?”

    The MOSAIC applicants and awardees are included in the overall counts for the K99 applicants and awardees. 28 Black K99 awardees is comprised of 16 MOSAIC awards and 12 original mechanism K99 awardees. Also, there could be applications that were submitted in March 2021 (FY 2021) and got funded in Nov. 2022 (FY 2022) or were submitted in July 2021 (FY 2021) and got funded in March 2022 (FY 2022).

  4. Re: Figure 4: Funding rates for K applicants according to race-ethnicity by fiscal year. Panel A shows data for all groups, while Panel B shows the same data but for White, Hispanic, and Black applicants only.
    Can you explain why Figure 4 separately reported Panel A and Panel B? Only difference is the removal of Asian and Unknown.
    And Figure 4 explains the Funding rate by race-ethnicity in FY2022. The funding rate for Black, White, Hispanic, and Asian look like around 45%, 37%, 34%, and 29%, respectively. Asian is the second largest race-ethnicity group that applied. But the funding rate for Asians is generally lower than for other race-ethnicity. What would be the main cause of the relatively low funding rate for Asians?

  5. Native Hawaiian or Other Pacific Islanders as well as American Indian or Alaska Native researchers were excluded from the report as noted in the blog and full length reports (linked at the end) due to small cell sizes. We do not publicly report sample sizes that are sufficiently small (<12), which is considered as potentially identifiable. Separately, our intent was to focus on African American/Black and Hispanic applicants in some of the panels here, but we will only show one panel (that includes Asian and other researchers) going forward on any future analyses.

    1. “Separately, our intent was to focus on African American/Black and Hispanic applicants in some of the panels here”. Does this mean NIH intentionally ignored Asians whose funding rate is the lowest?

    2. Why is NIH’s response to this matter excluding race and ethnicity based analysis going forward? One needs to measure the problem in order to identify the problem. So in the future, Asians will continue to have the lowest funding rate and NIH is going to hide it? At least continue to publsih data, so there is a record, instead of hiding it. This is like insisting there is no disparity when there is.

  6. “In 2022, The funding rate for Asian applicants is 36%, 22%, and 15% LOWER than those for Black, Hispanic, and White applicants, respectively.”

    I suggest the above conclusion be added to the report.

  7. For those wondering why removing the curve for Asians, the reasons are simple. Either because most Asian candidates are too excellent to accept a Mentored Career Award (they just can find opportunities elsewhere anyway), or just too bad to do research so better not waste money for their career training at all.

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