Analysis of NIH K99/R00 awards and the career progression of awardees

  1. Nicole C Woitowich
  2. Sarah R Hengel
  3. Christopher Solis
  4. Tauras P Vilgalys
  5. Joel Babdor
  6. Daniel J Tyrrell

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    This study follows the career trajectories of the winners of an early-career funding award in the United States, and finds that researchers with greater mobility, men, and those hired at well-funded institutions experience greater subsequent funding success. Using data on K99/R00 awards from the National Institutes of Health's grants management database, the authors provide compelling evidence documenting the inequalities that shape faculty funding opportunities and career pathways, and show that these inequalities disproportionately impact women and faculty working at particular institutions, including historically black colleges and universities. Overall, the article is an important addition to the literature examining inequality in biomedical research in the United States.

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Abstract

Many postdoctoral fellows and scholars who hope to secure tenure-track faculty positions in the United States apply to the National Institutes of Health (NIH) for a Pathway to Independence Award. This award has two phases (K99 and R00) and provides funding for up to 5 years. Using NIH data for the period 2006–2022, we report that ~230 K99 awards were made every year, representing up to ~$250 million annual investment. About 40% of K99 awardees were women and ~89% of K99 awardees went on to receive an R00 award annually. Institutions with the most NIH funding produced the most recipients of K99 awards and recruited the most recipients of R00 awards. The time between a researcher starting an R00 award and receiving a major NIH award (such as an R01) ranged between 4.6 and 7.4 years, and was significantly longer for women, for those who remained at their home institution, and for those hired by an institution that was not one of the 25 institutions with the most NIH funding. Shockingly, there has yet to be a K99 awardee at a historically Black college or university. We go on to show how K99 awardees flow to faculty positions, and to identify various factors that influence the future success of individual researchers and, therefore, also influence the composition of biomedical faculty at universities in the United States.

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  1. Version published to 10.7554/elife.88984.4 on eLife
  2. Version published to 10.7554/elife.88984 on eLife
  3. Version published to 10.7554/elife.88984.3 on eLife
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    Author Response

    The following is the authors’ response to the previous reviews.

    Recommendations for the authors:

    Reviewer #1 (Recommendations for the Authors):

    The authors have addressed my recommendations in the previous review round in a satisfactory way. I only have one additional comment to the authors:

    In the manuscript abstract lines 31-32, the author state that: "Using NIH data for the period 2006-2022, we report that ~230 K99 awards were made every year, representing ~$25 million annually."-- The "~$25 million" is under-stating the actual funds spent because this sum is just money spent on the first year of some k99s while the NIH is paying years 2,3,4 etc for others for k99 awards (~90% conversion rate to R00) awarded in previous years for a given year. The NIH is actually spending ~$230-$250 million a year on the k99 award mechanism in a given year. so the authors need to amend the stated amount in the manuscript.

    Thank you for pointing this out. The reviewer is correct, that we had incorrectly only calculated the investment $ in new K99 awards made. We have corrected this in the revised manuscript. We appreciate your careful reading of our manuscript and the edits made based on your comments have improved the final version.

    Reviewer #2 (Recommendations for the Authors):

    Thank you for taking the time to revise this important work. I learned a lot reading this paper a second time, and appreciate the improvements you have made.

    My only major thought while re-reading this is that I wish you all had written two papers! I see two themes in this work: one looking at faculty hiring networks from the Wapman et al. dataset, and another at K99/R00 conversions by institution, gender, and researcher mobility and its impact on subsequent funding success. After reading, I felt like I had many follow-up questions about both analyses, but it would be impractical for me to suggest all these follow-up analyses without making your paper unreasonably long.

    Thank you for these comments. We agree that there are 2 general themes in this paper. While we feel that significantly expanding on both themes will be important in future research. Our hope is that this work continues to inspire others to critically examine funding practices and inequity in the same way that the work of Wapman, Pickett, etc. inspired the present work.

    For example, regarding the results that more R00 are activated at different institutions, and that moving institutions improves subsequent funding success, I wonder: Do proportionally more women or men move institutions? Do proportionally more K99 awardees at less-funded places move for their R00, or less? The Cox proportional hazard models illustrate the impact of various characteristics on subsequent funding success, but they do not illustrate disparate impacts of mobility on different groups (if I am understanding them correctly). (You sort of dive into these questions in the very interesting subsection, "K99/R00 awardee self-hires are more common at institutions with top NIH funding." I wanted to read more!)

    Thank you for these kind comments. These are fantastic follow-up questions. We do not feel that we can adequately address them within the present manuscript without potentially splitting it into 2 separate manuscripts. However, we may examine these in future analyses. We are particularly interested in examining additional aspects such as how the K99 MOSAIC funding mechanism may differ from the traditional K99 mechanism. Since the K99 MOSAIC mechanism is newer, there may not be enough K99 MOSAIC awards made for a thorough exploration.

    As another example, for your analysis on faculty hiring networks, the prevalence of self-hiring amongst institutions and regions was one finding. However, this finding seems somewhat at odds with the previous takeaway about how researcher mobility improves subsequent funding success. Are institutions doing themselves a disfavor by hiring their own, then? I suspect there is more to say here about this pattern... maybe there are important differences between PhD institution and postdoc institution and its impact on hiring/subsequent funding success? Or is this a story about upward mobility into the top 25 well-funded NIH institutions?

    Again, these are very insightful comments and follow-up questions. We hope to address these in potential future manuscripts. We also hope that others may become interested in finding answers to these questions by exploring our dataset as well as other publicly available datasets such as the Wapman et al. dataset.

    I can completely understand how combining the faculty hiring network analysis with the K99/R00 conversions would seem like a natural fit, but I personally feel - emphasis on this being a personal opinion - that there would have been benefits to giving more space to the details of both analyses separately. Perhaps this is a "hindsight is 20/20" issue. Or an issue with the current times in which ones' brain can only hold so many main takeaways from a single body of work. (For example, I struggled to summarize your paper in my public review because I find so many takeaways important.)

    I suppose this is all to say that I find your work important enough to warrant additional follow-up work! :)

    Thank you for these very kind remarks. This work evolved over 8-10 months as evidenced by the updates to the biorXiv preprint. With unlimited time and foresight, it would probably be best to have separated the 2 themes into separate manuscripts and expanded both. Given current constraints, we plan to make some changes/updates to the present manuscript and hopefully include more in-depth analyses on each theme in future works. Thank you again for the thoughtful reading and critique of both our original manuscript and the revised version.

    Minor comments/questions:

    "K99 to R00 conversions are increasing in time"

    • Assuming I am interpreting the figures correctly, in my opinion, the most important takeaway is that the number of R00 awards have increased, but only for awardees moving to another institution. This key result, best illustrated by panels A and C of Figure 1, is buried in the long paragraph in this section. The organization of content in this section could be improved and more focused. Consider renaming this subsection to be more declarative: "K99 tR00 conversions have increased, but only for awardees moving to another institution."

    This is a very concise interpretation of this data. We have edited the paragraph referenced by the reviewer, split it into 2 paragraphs, and changed the title to “K99 awardees increasingly move to other institutions for R00 awards from 2008 to 2022” and the final sentence to “Thus, the number of K99 to R00 conversions is consistent over time, but increasingly more R00 awardees have moved to other institutions since 2013”

    • Similarly, I personally found the current title of the subsection, "K99 to R00 conversions are increasing with time" is mildly confusing. An R00 award indicates a successful conversion, so why not simply call this an R00 award instead of saying K99-to-R00 conversion? Also, when I look at Figure 1B and exclude the conversion rates for 2007 and 2008 (because this is a 3 year rolling average), I see that conversion rates (or R00 awards) have remained stagnant. This comment is very much in-the-weeds and is mainly to do with clarity of language.

    Thank you for these comments. We had “K99 to R00 conversion” to highlight the unique nature of this award mechanism that a person can only receive an R00 if they previously had a K99 award. Nevertheless, we have edited the text to “R00 awards” and “R00 awardees” to simplify things. We also want to note that we did not compute a 3-year rolling average. The function we used was: (X/(Y -1))x100 where X is the number of R00 awards made in a year and Y is the number of K99 awards made in a year. We did note an error in our calculation in the previous version of the manuscript. Previously, we included all R00 awards and K99 awards for each year from the NIH Reporter dataset; however, this is a flawed methodology. NIH reporter includes only extramural K99 award data and extramural R00 awards, but intramural K99 awardees can receive extramural R00 awards and thus are only included in the R00 dataset. There were 141 R00 awardees in our dataset from NIH Reporter that did not have K99 data, so we assume these are intramural K99 awards since it is required to have a K99 to be eligible for the R00 award. Since we do not know the awarding year for intramural K99 awardees or have data on intramural K99 awardees that fail to activate the R00 award (or stay internal at NIH), we have excluded these 141 R00 awardees. In the previous version, this mis-calculation exaggerated rolling conversion rate (we had correctly calculated the 78% total conversion rate). We re-analyzed our rolling conversion rate and found the average is 81.8% (excluding the first 2 years of the K99 program and the last 2 years).

    This is a long explanation, but essentially, we overestimated the number of R00 awards which inadvertently increased the rolling conversion rate. We have corrected this and simplified the first 2 paragraphs of the Results section.

    • I was also mildly confused looking at Figure 1c. The caption says that the percentages represent the K99 awardees that stayed at the same institution for the R00 activation, but the percentages are next to the solid circles which the legend labels as "different institution." Perhaps another or different way to show this is a stacked bar chart, where one bar represents the percentage of R00 awards activated at the same institution and another bar represents the percentage of R00 awards activated at a different institution. The bars always add to 100% but the change in proportions illustrates that proportionally fewer awards are being made to those remaining at the same institution.

    Great idea. We have included a stacked bar chart here. Since the stacked bar chart is percentages, we felt it was important to also show the total numbers so we still included the previous chart also but removed the percentage numbers from it. We also changed the departmental analysis to stacked bar charts. This shows the stark difference between 2008-2012 and 2013 onward. These changes were made in the revised Fig. 1.

    • Minor question: I would love to see Table 3 and Table 4 as a time-series. Has the proportion of recipients at various institution types changed with time?

    This is a great suggestion and we felt it fit best in Figure 5, so we’ve added it there.

    • Table 3 is useful but only indirectly addresses my first "Recommendation to the Authors" from my previous review. I did some number crunching myself from the data provided. Assuming I did this correctly: If you're a K99 awardee at a private institute, you had a 76.3% change of getting an R00 compared to 80.4% for a K99 awardee at a public institution. If you're a K99 awardee at a top-funded institution, you had a 76.8% chance of R00 compared to 78.6% for a lower-funded institution. I would have liked to see more figures and tables to illustrate conversion rates by institution type in this way. Interestingly, to me, these data suggest that there are not enormous conversion rate differences by institution type (though looking at these now, I am confused at the 89% statistic in line 174 and where that comes form, since it is much higher than what I've calculated).

    Thank you for this suggestion and these comments. Please see above where we describe how we incorrectly overestimated the 89% statistic. This has been corrected. As the reviewer suggested, we now show yearly percent of grants to specific institution types in the revised Figure 5. We agree with the reviewer that showing the conversion rate by institution type is interesting; however, it is fairly obvious from the new panels in Figure 5 that there is not much difference in conversion rate. Thus, to avoid crowding too many panels into the figure, we opted to keep the stacked bar plot.

    Reviewer #3 (Recommendations for the Authors):

    -One minor change to Figure 1C would be to switch the color coding for the lines so that they match with 1D whereby "same institution" would be white circles, or whatever the authors decide would be best for consistency since they are similar comparisons.

    Thank you for this suggestion. We have corrected this to be consistent.

    -Minor note for lines 459-461: I would suggest changing the wording to "intersectional inequalities" as it is not that a scientist's identities impact their careers as much as how those identities are positioned within an unequal opportunity structure and differentially treated that produce varying career trajectories and experiences of marginalization and cumulative (dis)advantages.

    Thank you and we agree with you. We have made this correction.

    -To carry forward a suggestion for the authors in my previous review, future research that more fully explores the research infrastructure of institutions for how top NIH funded institutions continue to be top funded institutions year after year could help clarify some of the career mobility and same/similar institution hiring found in the data. Rather than hand coding institutions for some of the infrastructure, the National Center for Education Statistics' Integrated Postsecondary Education Data System (IPEDS) has data on colleges and universities including whether they operate a hospital, have a medical degree, and many other interesting data about student and faculty demographics, institutional expenditures (including research budgets), and degrees awarded in different fields of study (undergrad and grad) that may be helpful to the authors as they continue their research stream in this area.

    Thank you very much. We will look into this data set as we continue our investigations in this area.

  5. eLife

    eLife assessment

    This study follows the career trajectories of the winners of an early-career funding award in the United States, and finds that researchers with greater mobility, men, and those hired at well-funded institutions experience greater subsequent funding success. Using data on K99/R00 awards from the National Institutes of Health's grants management database, the authors provide compelling evidence documenting the inequalities that shape faculty funding opportunities and career pathways, and show that these inequalities disproportionately impact women and faculty working at particular institutions, including historically black colleges and universities. Overall, the article is an important addition to the literature examining inequality in biomedical research in the United States.

  6. eLife

    Reviewer #1 (Public Review):

    Summary and strengths:

    This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

    The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (Merton, 1968: https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (Bol et al., 2018: https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

    Weaknesses:

    The authors have addressed my recommendations in the previous review round in a satisfactory way.

  7. eLife

    Reviewer #2 (Public Review):

    Summary and strengths:

    Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al., 2018, PNAS). In this study the authors examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 89% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions.

    Amongst the three characteristics, the finding that researcher mobility has the largest effect on subsequent funding success is key and novel. Other data supplement this finding: for example, although the total number of R00 awards has increased, most of this increase is for awards to individuals moving to different institutions. In 2010, 60% of R00 awards were activated at different institutions compared to 80% in 2022. These findings enhance previous work on the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

    These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

    The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

    At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark paper by Bol et al. (PNAS; 2018) that followed the careers of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

    Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/). These results are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. During the 2007-2017 period, the K99 award rate for white applicants was 31% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants. However, the work required to include these data may be beyond the scope of the study.

    The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

  8. eLife

    Reviewer #3 (Public Review):

    Summary:

    The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

    Strengths:

    The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the study by Wapman et al. (2022: https://www.nature.com/articles/s41586-022-05222-x), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

    Weaknesses:

    The authors have addressed my recommendations in the previous review round in a satisfactory way.

  9. Version published to 10.1101/2023.01.26.525751v16 on bioRxiv
  10. Version published to 10.7554/elife.88984.2 on eLife
  11. eLife

    Author Response

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public Review):

    This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

    The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

    Thank you for these comments. We have incorporated the articles referenced on the Matthew effect into the first paragraph of the Discussion our revised preprint.

    Reviewer #2 (Public Review):

    Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al. 2018, PNAS). Woitowich et al. examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 85% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions. This result is consistent with prior evidence of funding disparities by gender and institution type. The finding that researcher mobility has the largest effect on subsequent funding success is key and novel, and enhances previous work showing the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

    These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

    The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

    At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark Bol et al. (2018) paper that followed the careers of winners of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

    Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative: https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/. These results are not presently discussed in the paper, but are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. From 2007 to 2017, the K99 award rate for white applicants was 31.0% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants for the entire 2007-2017 period. And in terms of R00 awards, or successful faculty transitions: whereas 77.0% of white K99 awardees received an R00 award, the conversion rate for Asian and Black K99 awardees was lower, at 76.1% and 60.0%, respectively. Regarding this K99-to-R00 transition rate, Woitowich et al. found no difference by gender (Table 2). These results are consistent with a growing body of literature that shows that while there have been improvements to equity in funding outcomes by gender, similar improvements for achieving racial equity are lagging.

    The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

    One aspect that the authors should expand or comment on is the change in the rate of K99 to R00 conversions. Since 2016, while the absolute number of K99 and R00 awards has been increasing, the percentage of R00 conversions appears to be decreasing, especially in 2020 and 2021. This observation is not clearly stated or shown in Figure 1 but is an important point - if the effectiveness of the K99/R00 mechanism for postdoc-to-faculty transitions has been decreasing lately, then something is undermining the purpose of this mechanism. This result bears emphasis and potentially discussion for possible reasons for why this is happening.

    Thank you for these insightful comments. We now calculate a rolling conversion rate for K99 to R00 awards which shows there is not as much of a decline in conversion from K99 to R00 (Fig 1B). We still see a slight decline in 2021 and 2022. 468 K99 awards are from 2020 or later so they may still convert to the R00 phase. Thus it is difficult to draw conclusions about 2021/2022 yet. As more time passes, we may better be able to determine whether or not significant alteration from normal occurred in these years, presumably due to pressures from the Covid-19 pandemic. We also thank you for providing the details of the FOIA request. We have included a discussion of these data in the discussion.

    Reviewer #3 (Public Review):

    The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

    Strengths:

    The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the Wapman et al. study (among others from that research group), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses as discussed further below.). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

    Weaknesses:

    The authors reference institutional prestige in relation to some of the findings, but there's no specific measure of institutional prestige included in the analyses. If being identified as a top 25 NIH-funded institution is the proximate measure for prestige in the study, then more justification of how that relates to previous studies' measures of institutional prestige and status are needed to further clarify the interpretations offered in the manuscript.

    The identification of institutional funding disparities impacting HBCUs is an important finding and highlights another aspect of how faculty at these institutions are under resourced and arguably undervalued in their research contributions. However, a lingering question exists: why compare HBCUs with Harvard? What are the theoretical and/or methodological justifications for such comparisons? This comparison lends itself to reifying the status hierarchy of institutions that perpetuate funding and career inequalities at the heart of the current manuscript. If aggregating all HBCU faculty together, then a comparable grouping for comparison is needed, not just one institution. Perhaps looking at the top 25 NIH funded institutions could be one way of providing a clearer comparison. Related to this point is the confusing inclusion of Gallaudet in Figure 6 as it is not an officially identified HBCU. Was this institution also included in the HBCU-related calculations?

    Thank you for this comment. We agree this comparison perpetuates the perception of the prestige hierarchy and is problematic. We now compare all institutions in the top 25 NIH funding category to all HBCUs. Thank you also for identifying our error in mis-coding Gallaudet as an HBCU. We have corrected this in the current version.

    There is a clear connection that is missed in the current iteration of the manuscript derived from the work of Robert Merton and others about cumulative advantages in science and the "Matthew effect." While aspects of this connection are noted in the manuscript such as well-resourced institutions (those with the most NIH funding in this circumstance) hire each others' K99/R00 awardees, elaborating on these connections are important for readers to understand the central processes of how a rigid hierarchy of funding and career opportunities exist around these pathways. The work the authors build on from Daniel Larremore, Aaron Clauset, and their colleagues have also incorporated these important theoretical connections from the sociology of knowledge and science, and it would provide a more interdisciplinary lens and further depth to understanding the faculty career inequalities documented in the current study.

    Reviewer #1 (Recommendations For The Authors):

    Comments to authors:

    1. For the benefit of general reader, it would be informative to mention the amount of annual NIH investment in the k99 funding mechanism in the text (230 awards representing a ~ 230 million US dollars investment).

    Thank you for this suggestion. We have added that this is ~$25 million investment annually.

    1. It is worth noting that some of the observed funding and hiring traits resemble the Matthew effect, discussed in: The Matthew effect in science: https://www.science.org/doi/10.1126/science.159.3810.56

    The Matthew effect in science funding: https://www.pnas.org/doi/10.1073/pnas.1719557115

    It would be of value to cite these for further context for the readers.

    Thank you for this suggestion. We have included these references and briefly discussed the Matthew effect in the first paragraph of the Discussion.

    1. Figs 3, 6 and Fig S1 are hard to read without zooming in due to their format and don't work great within a letter size page but can work if they are also linked to a zoomable web version. It would make sense to have an online navigable/searchable/selectable version. But when the reader zooms out, there are patterns that reflect what points the authors are making (though those could be illustrated differently). These figures are really made for online webapp visualization (such as Shiny in R).

    We agree with this comment and have used the “googleVis()” package in R to put together interactive Sankey diagrams. These can be found at: https://dantyrr.github.io/K99-R00-analysis/ and they are referenced in the manuscript.

    1. The abstract states 85% of awardees get R00 awards. That appears to come from 198/234 (page 6) though it's not explicitly stated, and other ratios give different answers (e.g., 1-304/3475 = 91%) but the 85% seems to be the right one. That first paragraph of the results could be clearer. Also, in the middle of page three the number given is 90% so something is inconsistent. For Figure 1A, given the methodology it should be possible to calculate a rolling conversion rate as "R00(t) / K99(t-1)" (and a similarly-calculated cumulative rate).

    Thank you for catching these errors. These were introduced because there are R00 awardees that did not have extramural K99 awards. These are intramural NIH K99 awardees but there is no public data on these awardees. The correct number is 78% of K99 awardees that transitioned to the R00 phase. We have also calculated the rolling conversion rate which is 89% if you exclude the first 2 years of the program (when the first awardees were within the 2-yr K99 period) and final 2 years (when most recent K99 awardees were still within their first 2 years of the K99 period).

    1. Assuming that 85% is the correct number, is there any information/insight into why ~1/6 of awardees do not continue to R00, which seems high given that only two years passes - that's a lot of awardees not getting R00 positions.

    We are unsure of why these don’t convert. In the revised version of the manuscript, we speculate on this in the 4th paragraph of the discussion:

    The factors that prevented the other 302 K99 awardees from 2019 and earlier unable to convert their K99-R00 grants is cause for concern within our greater academic community. Possible explanations include leaving the biomedical workforce, accepting tenure-track positions or other positions abroad, or by simply not successfully securing a tenable tenure-track offer.

    1. It looks like perhaps a non-zero number of K99s are just one year and not two (e.g., see 2006 in Fig 1A, which should not appear if all 2006 awards were 2 years). What is the typical percentage of K99s not activated for a second year, and is this a sizable % of the 15% not converting to R00?

    This is an interesting question. We didn’t originally look into this and the dataset that we originally downloaded from NIH reporter included a significant number of duplicates for the grants because year 1 of the K99 was listed on its own line and year 2 was listed on a different line. The first step in curating the data was to delete the duplicate values so we only had one entry per person. Unfortunately based on sorting of the data tables, sometimes the year 1 appeared above year 2 and at other times year 2 appeared before year 1. Because none of the data we were interested in are benchmarked to K99 start date, we removed the duplicate values non-specifically. With the dataset we currently have, we would not be able to tell which individuals dropped out (didn’t convert to R00) during the first or second year of the K99. In order to do this we would have to download the raw data from NIH reporter again and curate it again. We may do this in the future but for the purpose of publishing the current manuscript we prefer to focus our efforts on other aspects of the revision.

    1. Further down page 3, the authors state that "men typically experience 2-3% greater funding success rates" is ambiguous, as rates are themselves a percentage. So, is it 2-3% greater as in 23% vs 20%, or is it 2-3% greater as in 20.6% vs 20%? Please clarify the language.

    Thank you for asking for this clarification. We have updated the text here to reflect that we mean “23% vs 20%”.

    1. Metrics such as time to first R01 are compared internally within the study set, which yields interesting insights, but more could be done to benchmark these metrics to non-K99 scientists.

    We agree with the reviewer that this would be ideal; however, we feel that it is out of the scope of this manuscript. We may examine this in the future.

    1. In the text, several times percentages are being referred to when the figures cited do not show percentages. For example (page 6) 'proportion of awardees that stayed at the same institution declined to about 20% where it has remained consistent (Fig 1B)' - Figure 1B does not show percentages, instead the reader would need to work out from the raw numbers what the pattern of percentages might look like. It's fine (great even) to provide the raw numbers, but would be great to show the percentages as well. This happened for multiple graphs.

    Thank you for this comment. We agree that showing the percentage would be beneficial so we have included the percentages in Figure 1 for the conversion rate. We also added a standalone figure panel for the rolling conversion rate for Figure 1. For Figure 4, we have also included a right Y-axis to better indicate the % women.

    1. Figure 4 - putting the %women on a 0-250 scale makes it difficult to see the changes in that curve. Please replot it as a separate graph with an appropriate scale (30-50%? 30-70%?)

    Thank you for this comment. We have made this edit.

    1. Figure 5 - The table appears inconsistent - the Moved/Stayed HR is 1.411 suggesting that moving is better for reducing time to R01, but then Woman/Man is 1.208, so one of these pairs needs to be written in the opposite order to have the table make sense (intended to be listed as 'better/worse'?)

    Thank you for noticing this. In the revised manuscript we have re-run the cox proportional hazard model using the R package “survival” and the function “coxph()”. There were minor differences in the hazard ratios using this package instead of Graphpad prism; however, the R package is much more widely used compared to prism for these types of analysis. We present the new data in the table in Figure 5B in the revised manuscript. We now present the “detrimental” cox hazard value for each variable (i.e. 0.7095 for the mobility [moved/stayed]). We also underlined the variable which was detrimental to receiving an R01 award earlier.

    1. Figure 5's graph appears strange. All the lines have an appearance of stochasticity but are actually multiples of each other, rising exactly in sync. Are these actually modeled lines? If so, why not instead actually draw the lines based on the real data from the real groups depicted, and give the n for each group?

    Thank you for picking this up. The software we originally used to plot the graphs did plot modeled lines instead of the actual data. We have re-run the cox proportional hazard model using the R “survival” package v3.5-5 and the coxph() and survfit() functions. The updated data are in Figure 5 of the revised manuscript.

    1. Table 1 should note that each column sums to 100%.

    This is a good suggestion. In the revised manuscript, we have added a row to the table to indicate the column total N and %.

    1. The authors discuss how k99/R00 grant reviewing process may have to change but the k99 awards also impact the faculty hiring ecosystem as well. There are faculty hiring job ads explicitly requesting or indicating preference towards k99 holders and the results described in this article show that k99 awarding is biased towards particular demographics at select wealthy institutions. Of course, collective/central action is almost always more effective/impactful (especially in shorter time line) than individual elective action. In other words, NIH changing granting patterns would likely work better than encouraging faculty searches to change the weight they give to K99s, because there are many searches and just one NIH. But these are not mutually exclusive and individual action can still help when central action isn't done (if the NIH does not change the k99/R00 grant review process for more inclusive funding and does not increase the number of annual k99 awards hence the annual budget for this award mechanism) and it would be good to have this discussed in the manuscript.

    Thank you for this comment and thoughtful insights. We have included additional discussion on this in the final paragraph of the discussion.

    Reviewer #2 (Recommendations For The Authors):

    Thank you for conducting this important work. On top of some thoughts I have described in the public review (in particular, Chris Pickett's FOIA data on K99/R00 outcomes by applicant race and ethnicity), I only have a few comments for potential improvements to this paper:

    1. The comparison of K99-R00 transition rates by gender was interesting. However, I missed the analysis on the K99-R00 transition rates by institution (by type or by top-25 NIH funded institution versus not). I think this analysis may be buried somewhere in the more nuanced descriptions about faculty flows from one institution type to another, but I was not able to locate it. I wonder if the authors could consider dedicating a subsection to specifically describing the transition rate by institution type, creating a table equivalent to Table 2. This section would probably fit best somewhere before the authors dive into the nuances of self-hires and faculty flows.

    Said another way: As I was reading, I felt I was missing an answer to a simple question - are there differences in conversion rates by institution type (however you define institution type, as an MSI or non MSI, or top-25 NIH funded versus not)?

    Thank you for this suggestion. We have created the table (Table 3 and Table 4) in the revised manuscript. We also made a new figure (now figure 5 in the revised manuscript). This was an interesting way to look at the data and it is very clear that the number of K99 and R00 awards is heavily concentrated within the institutions that have the highest NIH funding. We have added a paragraph in the results in a new section entitled “K99 and R00 awards are concentrated within the highest funded institutions”.

    1. Regarding the comparison of HBCUs and Harvard: this analysis was elucidating, but I am not sure if the framing of this analysis as pertaining to "systematically marginalized groups" - see second sentence in the section, "Faculty doctorates differ between Harvard and HBCUs" is appropriate. While it is true that proportionally more faculty at HBCUs are from marginalized groups, there are also many faculty at HBCUs who are from privileged or advantaged backgrounds (e.g., white, men, educated at elite institutions). It would be more accurate to rephrase the second sentence to say something along the lines of, "We sought to examine the rates of funding for those at historically under-funded institutions." I recommend that the authors comb the paper for any other potential places in the text that conflate systemic marginalization with institution type, and rephrase as needed for accuracy.

    Thank you for pointing this out. This is an extremely important point and we have removed any instances we could find where we conflate systemically marginalized groups with institution type.

    1. I strongly recommend Sugimoto and Larivière (2023)'s new book, Equity for Women in Science, which has an entire section dedicated to previous work investigating how researcher mobility impacts access to resources, collaborations, et cetera (Chapter 5 on Mobility; other chapters on Funding are also relevant but I hone in on Mobility since this is such a key result of this work). I think this chapter would provide significant food-for-thought and background that could strengthen the Discussion section of the paper.

    Thank you for this suggestion. We have added some discussion of mobility in the first paragraph of the Discussion.

    1. I appreciated the subsection headings that described key results (e.g., "Institutions with the most NIH funding tend to hire K99/R00 awardees from other institutions with the most funding"; "K99/R00 awardee self-hires are more common at institutions with the top NIH funding.") This paper structure made it easier for me to ensure that I was getting the intended takeaway from a figure or section. But partway through the paper, the subheadings changed to being less declarative and therefore less informative (e.g., "Gender of K99/R00 awardees"; "Factors influencing K99/R00 awardee future funding success"). It would be great to rephrase these boilerplate subsection headers to be more declarative, like earlier subsection headings. For example, maybe say "Men receive the majority of K99 awards" or "No gender difference in the rate of conversion from K99 to R00" or something to that effect, depending on what result the authors wish to emphasize.

    Thank you for this comment. This is a very good point. We have re-worded the more generic headings in the revised version.

    1. Lastly, I would like to share a question that came to my mind that involves an additional analysis, but is work that is (probably) out-of-the-scope of this paper, but could instead be a separate paper or product. Circling back to Chris Pickett's FOIA-ed data on K99/R00 funding outcomes by applicant race and ethnicity (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/): Given that Pickett's numbers provide incontrovertible information on the number of awards to various racial and ethnic groups, I wonder if it is possible to use this information as an "answer key" to (1) check the accuracy of an algorithm that assigns race based on name for applications in your analysis but for 2007-2017 period, and, (2) if the results are reasonable, then examine the dataset with race and ethnicity information. Some recent papers performing large-scale bibliometric analyses have applied such algorithms (e.g., see Kozlowski et al. 2022 PNAS Intersectional inequalities in science) and I wonder if they could be useful, or at least tested, here. Again, Pickett's data would serve as the benchmark to see if the algorithm produces numbers that are consistent with the actual funding outcomes; if they're not wildly off, or perhaps accurate for some groups but not others, there might be something here.

    This is a really insightful comment. We have discussed whether we could assign ethnicity based on an algorithm and check based on Chris Pickett’s data. We agree that it is beyond the scope of this article, but has potential for future research.

    Reviewer #3 (Recommendations For The Authors):

    -In the methods section, it would be helpful to provide an overview of the number of universities, departments, and faculty represented in the data analyzed in the study.

    Thank you for this comment. We agree with the reviewer. We have added a section to the results discussing the distribution of different types of institutions. We also added Table 3 and Table 4 and a new Figure 5 describing these. Regarding the faculty, we have discussed the demographics of the K99 and R00 awardees as best as we could. We do not have data on which faculty laboratories the K99 awardees were in when they received their awards. This information is not available through NIH reporter.

    -I would consider incorporating, or at least citing, Jeff Lockhart and colleagues' recent paper Nature Human Behavior article "Name-based demographic inference and the unequal distribution of misrecognition" about to provide readers with an additional resource and more information about the likelihood of misattribution and general cautionary notes about using gender and race/ethnicity ascription/imputation approaches and tools for research.

    Thank you for bringing this reference to our attention. We have incorporated this into the methods section describing our name-based gender determination.

    -In the next to last sentence under the final paragraph of the methods section, there looks to be a typo as it should read "K99 or R00," not "K00" as currently written.

    Thank you for catching this. We have now corrected it.

    -Clarifying some of the data and measures used are necessary to limit confusion and misinterpretations of the study's findings.

    Thank you. We have significantly updated the revised manuscript and hope that it is more clear.

    -Elaborating more on the gender inequality notable in the Cox proportional hazard model would strengthen the authors' point about persistent gender inequalities within the K99/R00 funding mechanism and pathways. In its current iteration, the findings are somewhat buried by the discussion of institutional differences, but when we look at the findings and the plot associated with the model, we notice that men have more advantages than women in funding and institutional location.

    Thank you for highlighting this. This is true and we have elaborated on the gender inequality in the revised version of the manuscript.

    -Also for the Cox proportional hazard model, I would consider exploring the inclusion of data that can further clarify the biomedical research infrastructure of institutions. For example, in the conversation about the differences between Princeton and other universities including other Ivies, it's important to note that Princeton does not have a medical school. Moreover, other institutions do not operate or are affiliated with a hospital. Adding more data to the model that can better contextualize the research infrastructure around researchers with NIH awards beyond the size of the NIH portfolio can shed light on possibly other important institutional differences that undergird these inequalities.

    Thank you for this comment. We have added additional details about the institutional type; however, to examine whether institutions are attached to a hospital (or are themselves as hospital like MGH etc.) or whether institutions include a medical school may be difficult. We would have to manually code these and then determine whether or not the award recipient was affiliated with a department within that entity or not. We believe that this is a fascinating question but that it is out of the scope of the present manuscript. This is something that we will look into for potential future publications.

    -Throughout the manuscript there's usage of "elite" and "prestigious" that are somewhat ambiguous regarding what exactly they are referring to about institutional characteristics. This is a common issue in the literature, but trying to clarify what these terms specifically mean for the current study and checking for consistent usage with limited interchangeability that can add confusion for readers about what is being referred to would give added strength to the conversation provided by the authors.

    Thank you for this suggestion. Based on these comments and those by the other reviewers, in the revised version of the manuscript, we have limited the use of “elite” and “prestigious” to describe institutions in order not to perpetuate biases toward certain institutions.

    -In relation to the discussion at the end of the manuscript of the longer time to award noted for researchers who stay at the same institutions, another possibility for the disparity could be their reliance for service work (e.g., hiring committees, departmental committees, supporting graduate students through mentoring and/or dissertation committee work, etc.) in their institutions given their knowledge of and experience within it.

    Thank you for this suggestion. We have added 2 sentences to the discussion reflecting this possibility.

    -Engaging with how STEM professional cultures can perpetuate these funding disparities and related hiring and career outcomes could enhance the contributions of the study. In relation to STEM professional cultures, engaging with the work of Mary Blair-Loy and Erin Cech in their recent book, Misconceiving Merit, could help provide additional insights for readers.

    Thank you for these comments. We have incorporated edits to the revised manuscript reflecting the work of Erin Cech and Mary Blair-Loy.

  12. eLife

    eLife assessment

    This study follows the career trajectories of the winners of an early-career funding award in the United States, and finds that researchers with greater mobility, men, and those hired at well-funded institutions experience greater subsequent funding success. Using data on K99/R00 awards from the National Institutes of Health's grants management database, the authors provide compelling evidence documenting the inequalities that shape faculty funding opportunities and career pathways, and show that these inequalities disproportionately impact women and faculty working at particular institutions, including historically black colleges and universities. Overall, the article is an important addition to the literature examining inequality in biomedical research in the United States.

  13. eLife

    Reviewer #1 (Public Review):

    Summary and strengths
    This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

    The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (Merton, 1968: https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (Bol et al., 2018: https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

    Weaknesses
    The authors have addressed my recommendations in the previous review round in a satisfactory way.

  14. eLife

    Reviewer #2 (Public Review):

    Summary and strengths
    Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al., 2018, PNAS). In this study the authors examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 89% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions.

    Amongst the three characteristics, the finding that researcher mobility has the largest effect on subsequent funding success is key and novel. Other data supplement this finding: for example, although the total number of R00 awards has increased, most of this increase is for awards to individuals moving to different institutions. In 2010, 60% of R00 awards were activated at different institutions compared to 80% in 2022. These findings enhance previous work on the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

    These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

    The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

    At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark paper by Bol et al. (PNAS; 2018) that followed the careers of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

    Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/). These results are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. During the 2007-2017 period, the K99 award rate for white applicants was 31% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants. However, the work required to include these data may be beyond the scope of the study.

    The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

  15. eLife

    Reviewer #3 (Public Review):

    Summary
    The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

    Strengths
    The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the study by Wapman et al. (2022: https://www.nature.com/articles/s41586-022-05222-x), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

    Weaknesses
    The authors have addressed my recommendations in the previous review round in a satisfactory way.

  16. Version published to 10.1101/2023.01.26.525751v15 on bioRxiv
  17. Version published to 10.1101/2023.01.26.525751v14 on bioRxiv
  18. Version published to 10.1101/2023.01.26.525751v13 on bioRxiv
  19. Version published to 10.1101/2023.01.26.525751v12 on bioRxiv
  20. Version published to 10.7554/elife.88984.1 on eLife
  21. eLife

    eLife assessment

    This study follows the career trajectories of the winners of an early-career funding award in the United States, and finds that researchers with greater mobility, men, and those hired at well-funded institutions experience greater subsequent funding success. Using data on K99/R00 awards from the National Institutes of Health's grants management database, the authors provide convincing evidence documenting the inequalities that shape faculty funding opportunities and career pathways, and show that these inequalities disproportionately impact women and faculty working at particular institutions, including historically black colleges and universities. Overall, the article is an important addition to the literature examining inequality in biomedical research in the United States.

  22. eLife

    Reviewer #1 (Public Review):

    This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

    The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

  23. eLife

    Reviewer #2 (Public Review):

    Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al. 2018, PNAS). Woitowich et al. examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 85% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions. This result is consistent with prior evidence of funding disparities by gender and institution type. The finding that researcher mobility has the largest effect on subsequent funding success is key and novel, and enhances previous work showing the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

    These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

    The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

    At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark Bol et al. (2018) paper that followed the careers of winners of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

    Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative: https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/ These results are not presently discussed in the paper, but are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. From 2007 to 2017, the K99 award rate for white applicants was 31.0% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants for the entire 2007-2017 period. And in terms of R00 awards, or successful faculty transitions: whereas 77.0% of white K99 awardees received an R00 award, the conversion rate for Asian and Black K99 awardees was lower, at 76.1% and 60.0%, respectively. Regarding this K99-to-R00 transition rate, Woitowich et al. found no difference by gender (Table 2). These results are consistent with a growing body of literature that shows that while there have been improvements to equity in funding outcomes by gender, similar improvements for achieving racial equity are lagging.

    The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

    One aspect that the authors should expand or comment on is the change in the rate of K99 to R00 conversions. Since 2016, while the absolute number of K99 and R00 awards has been increasing, the percentage of R00 conversions appears to be decreasing, especially in 2020 and 2021. This observation is not clearly stated or shown in Figure 1 but is an important point - if the effectiveness of the K99/R00 mechanism for postdoc-to-faculty transitions has been decreasing lately, then something is undermining the purpose of this mechanism. This result bears emphasis and potentially discussion for possible reasons for why this is happening.

  24. eLife

    Reviewer #3 (Public Review):

    The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

    Strengths:

    The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the Wapman et al. study (among others from that research group), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses as discussed further below.). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

    Weaknesses:

    The authors reference institutional prestige in relation to some of the findings, but there's no specific measure of institutional prestige included in the analyses. If being identified as a top 25 NIH-funded institution is the proximate measure for prestige in the study, then more justification of how that relates to previous studies' measures of institutional prestige and status are needed to further clarify the interpretations offered in the manuscript.

    The identification of institutional funding disparities impacting HBCUs is an important finding and highlights another aspect of how faculty at these institutions are under resourced and arguably undervalued in their research contributions. However, a lingering question exists: why compare HBCUs with Harvard? What are the theoretical and/or methodological justifications for such comparisons? This comparison lends itself to reifying the status hierarchy of institutions that perpetuate funding and career inequalities at the heart of the current manuscript. If aggregating all HBCU faculty together, then a comparable grouping for comparison is needed, not just one institution. Perhaps looking at the top 25 NIH funded institutions could be one way of providing a clearer comparison. Related to this point is the confusing inclusion of Gallaudet in Figure 6 as it is not an officially identified HBCU. Was this institution also included in the HBCU-related calculations?

    There is a clear connection that is missed in the current iteration of the manuscript derived from the work of Robert Merton and others about cumulative advantages in science and the "Matthew effect." While aspects of this connection are noted in the manuscript such as well-resourced institutions (those with the most NIH funding in this circumstance) hire each others' K99/R00 awardees, elaborating on these connections are important for readers to understand the central processes of how a rigid hierarchy of funding and career opportunities exist around these pathways. The work the authors build on from Daniel Larremore, Aaron Clauset, and their colleagues have also incorporated these important theoretical connections from the sociology of knowledge and science, and it would provide a more interdisciplinary lens and further depth to understanding the faculty career inequalities documented in the current study.

  25. Version published to 10.1101/2023.01.26.525751v11 on bioRxiv
  26. Version published to 10.1101/2023.01.26.525751v10 on bioRxiv
  27. Version published to 10.1101/2023.01.26.525751v9 on bioRxiv
  28. Version published to 10.1101/2023.01.26.525751v8 on bioRxiv
  29. Version published to 10.1101/2023.01.26.525751v5 on bioRxiv
  30. Version published to 10.1101/2023.01.26.525751v6 on bioRxiv
  31. Version published to 10.1101/2023.01.26.525751v7 on bioRxiv
  32. Version published to 10.1101/2023.01.26.525751v4 on bioRxiv
  33. Version published to 10.1101/2023.01.26.525751v3 on bioRxiv
  34. Version published to 10.1101/2023.01.26.525751v1 on bioRxiv
  35. Version published to 10.1101/2023.01.26.525751v2 on bioRxiv