Over the past few years, there has been increasing interest (Peifer, 2017) in how the National Institutes of Health (NIH) funding support is distributed, with concern voiced by some that there may be excess concentration of support given to men and to the most well-funded late-career investigators. In a report (National Institutes of Health, 2019) issued by an NIH Working Group to the Advisory Committee to the Director (ACD), it was noted that "In biomedical science, power stems from who has access to awards. The Working Group heard repeatedly that the concentration of funding in a relatively small number of investigators (who are overwhelmingly white, cisgender, straight men) incentivizes universities to protect researchers bringing in high levels of grant funding".

Recently published literature has raised concerns regarding how NIH distributes funding support. One report (Katz and Matter, 2020) which focused on all ‘R’ grants found increasing inequality of funding support over 30 years (1985–2015). A research letter (Oliveira et al., 2019) found lower levels of support for grants in which women were identified as Principal Investigators. Other reports have documented disproportionate aging of the research workforce (Blau and Weinberg, 2017) and stresses particular to mid-career investigators (Charette et al., 2016); these reports are concerning given evidence that there is no correlation between research stage and scientific impact (Sinatra et al., 2016).

In 2017, the NIH considered imposing a cap (Lauer, 2017a) on individual-investigator research support through use of a ‘Grant Support Index or GSI’ (Lauer, 2017b) which classified grants according to mechanism (e.g. R01, P01, U54) rather than according to dollars. The GSI set a value of 7 for R01 grants, with lower values for ‘smaller’ mechanisms like R03 or R21 and greater values for mechanisms like P01 or U54. The proposed cap was set at 21, meaning that on average no investigator could be designated as PI on more than the equivalent of three R01 grants. The proposed cap was highly controversial (Kaiser, 2017) and was dropped in favor of a different approach (Lauer et al., 2017) that targeted funds directly toward early career investigators.

Here, we present updated data on distribution of NIH support for principal investigators (‘PIs’, keeping in mind that NIH issues awards to institutions [Lauer, 2018], not to individual scientists) with particular attention to career stage, gender, race, and degree. We focus on research project grants (‘RPGs’) as these comprise close to 80% of all NIH extramural research funding; we can also assess patterns that are independent of already well-known disparities for small business and non-RPG research grants.

Inequalities in funding of RPG PIs have increased since the NIH doubling, with further increases since sequestration in 2013 (Figure 1). Over the past few years, a time of substantial and sustained budget increases for NIH and a time of focus on early career investigators, there has been a decrease in the degree of inequality, but not quite back to the level of 2013. The RPG funding inequalities primarily reflect changes ‘at the top,’ meaning among the most highly funded investigators (Figure 1, Panels C and D). The top 1%’s share of RPG funding has increased from 8% before the doubling to nearly 10% now (Figure 1, Panel C); this difference translates into ~$400 million, or the equivalent of 800 RPG awards. Since sequestration, the top 1% has received an increased share of funding, while the bottom 50% has received less. During the NIH doubling, both the top centile and the lower half saw increases in the proportion of funding they received (Figure 1, Panels A and B).

The composition of the RPG PI workforce has evolved over time, with greater proportions of investigators who are late career, women, and Asian, and lesser proportions of MD-only degree holders (Figure 2). Despite steady increases in the proportion of women investigators, they are still well below parity. (Figure 2, Panel B). Among the groups studied, more funding goes to late career investigators, as well as to men, whites, and holders of MD degrees. Nonetheless, there is greater inequality within groups than between groups (Figures 3–5). One might argue that it may be reasonable for researchers to receive more funding at later career stages as they may have larger networks and are more experienced at posing research questions. Thus, some inequality may be considered ‘acceptable.’ But there is not funding parity for gender or race for researchers in the workforce, which are unacceptable inequalities. Over the past few years NIH has launched high-profile initiatives to enhance the diversity of the biomedical research work force. (National Institutes of Health, 2021b; ORWH, 2021).

From the NIH IMPAC II database, we obtained PI-specific data on inflation-adjusted total-cost funding of Research Project Grants (RPGs), defined as those grants with activity codes of DP1, DP2, DP3, DP4, DP5, P01, PN1, PM1, R00, R01, R03, R15, R21, R22, R23, R29, R33, R34, R35, R36, R37, R61, R50, R55, R56, RC1, RC2, RC3, RC4, RF1, RL1, RL2, RL9, RM1, UA5, UC1, UC2, UC3, UC4, UC7, UF1, UG3, UH2, UH3, UH5, UM1, UM2, U01, U19, and U34. Not all of these activity codes were used by NIH every year. For FY 2009 and 2010, we excluded awards made under the American Recovery and Reinvestment Act of 2009 (ARRA) and all ARRA solicited applications and awards. For FY2020, we excluded awards issued using supplemental Coronavirus (COVID-19) appropriations. Inflation-adjustments were referenced to FY2019 using the Biomedical Research and Development Price Index (National Institutes of Health, 2021a).

We measured inequality by three approaches: Proportion of funds going to the top 1%,or centile, (Saez and Zucman, 2020); standard deviation of the log of funding (Hoffmann et al., 2020), a measure that accounts for the well-documented skewness in funding and that is particularly sensitive to low and intermediate levels of funding; and the Theil T index (Conceição and Ferreira, 2000), a measure that is more sensitive to higher levels of funding and that be exploited to explore contributions of different groups to overall inequality.

For individual level data (say of individual PIs), the Theil Index (T) of funding inequality is mathematially represented as:

where n is the number of individual PIs, y_p is the funding of PI p, and ${\mu }_y$ is the population mean funding. The final logarithmic fraction takes on a value greater than 0 if the individual investigator p’s funding is greater than the population mean ${\mu }_y$ and less than 0 if the individual investigator’s funding is less than the population mean. We can think of the three terms as: $\frac{1}{n}$ as the investigator’s proportion of the population; $\frac{y_p}{{\mu }_y}$ as the magnitude of deviance compared to the overall population; and $ln\frac{y_p}{{\mu }_y}$ as the direction of deviance.

For grouped data (e.g. data grouped by career stage or gender or other characteristics), we can present the Theil Index T as a weighted average of inequality within each group plus inequality between those groups. That is:

where $T_g^{\prime }$ is the between-group component and $T_g^w$ is the within group component.

The between-group component of the Theil Index ($T_g^{\prime }$) is mathematically represented in a form similar to the overall Theil Index (Equation 1), namely:

where i indexes the m groups (e.g. early, middle, and late career investigators), P is the total population, y_i is the average funding of the group i, and μ is the average funding accross the entire population. The expression within the parenthesis is called the ‘Theil element,’ which is positive (or negative) if the group’s average funding is above (or below) the population average and zero if the averages are equal. The Theil elements represent the contribution of each group to total inequality between the groups.

Unlike other measures of inequality (e.g. proportion of funding going to the top centile or standard deviation of log funding), the Theil Index is not intuitive. However, it can be used to parse group data, allowing us to parse inequality into within group and between group componentd between group component.

Source data have been provided in R format. R markdown source code corresponds with all numbers, tables, and figures.

1. 1. Blau DM
   2. Weinberg BA

   (2017) Why the US science and engineering workforce is aging rapidly

   PNAS 114:3879–3884.

   https://doi.org/10.1073/pnas.1611748114

   • PubMed
   • Google Scholar
2. 1. Charette MF
   2. Oh YS
   3. Maric-Bilkan C
   4. Scott LL
   5. Wu CC
   6. Eblen M
   7. Pearson K
   8. Tolunay HE
   9. Galis ZS

   (2016) Shifting demographics among research project grant awardees at the national heart, lung, and blood institute (NHLBI)

   PLOS ONE 11:e0168511.

   https://doi.org/10.1371/journal.pone.0168511

   • PubMed
   • Google Scholar
3. Software

   1. Conceição P
   2. Ferreira P

   (2000) The Young Person’s Guide to the Theil Index: Suggesting Intuitive Interpretations and Exploring Analytical Applications

   The Young Person’s Guide to the Theil Index: Suggesting Intuitive Interpretations and Exploring Analytical Applications.

   http://utip.gov.utexas.edu/papers/utip_14.pdf
4. 1. Hoffmann F
   2. Lee DS
   3. Lemieux T

   (2020) Growing income inequality in the united states and other advanced economies

   Journal of Economic Perspectives 34:52–78.

   https://doi.org/10.1257/jep.34.4.52

   • Google Scholar
5. 1. Kaiser J

   (2017) Updated: nih abandons controversial plan to cap grants to big labs, creates new fund for younger scientists

   Science 4:aan6947.

   https://doi.org/10.1126/science.aan6947

   • Google Scholar
6. 1. Katz Y
   2. Matter U

   (2020) Metrics of inequality: the concentration of resources in the U.S. biomedical elite

   Science as Culture 29:475–502.

   https://doi.org/10.1080/09505431.2019.1694882

   • Google Scholar
7. 1. Lauer M
   2. Tabak L
   3. Collins F

   (2017) Opinion: the next generation researchers initiative at NIH

   PNAS 114:11801–11803.

   https://doi.org/10.1073/pnas.1716941114

   • PubMed
   • Google Scholar
8. Software

   1. Lauer MS

   (2017a) Implementing Limits on Grant Support to Strengthen the Biomedical Research Workforce – NIH Extramural Nexus

   Implementing Limits on Grant Support to Strengthen the Biomedical Research Workforce – NIH Extramural Nexus.

   https://nexus.od.nih.gov/all/2017/05/02/nih-grant-support-index/
9. Software

   1. Lauer MS

   (2017b) Research Commitment Index: A New Tool for Describing Grant Support

   Research Commitment Index: A New Tool for Describing Grant Support.

   https://nexus.od.nih.gov/all/2017/01/26/research-commitment-index-a-new-tool-for-describing-grant-support/
10. Software

    1. Lauer MS

    (2018) “Wait…It’s Not MY Grant?

    “Wait…It’s Not MY Grant?.

    https://nexus.od.nih.gov/all/2018/05/29/waitits-not-my-grant/
11. Software

    1. National Institutes of Health

    (2019) Working Group Report to the Advisory Committee to the NIH Director: Changing the Culture to End Sexual Harassment

    Working Group Report to the Advisory Committee to the NIH Director: Changing the Culture to End Sexual Harassment.

    https://acd.od.nih.gov/documents/presentations/12122019ChangingCulture_Report.pdf
12. Software

    1. National Institutes of Health

    (2021a) Price Indexes

    Price Indexes.

    https://officeofbudget.od.nih.gov/gbipriceindexes.html
13. Software

    1. National Institutes of Health

    (2021b) UNITE

    UNITE.

    https://www.nih.gov/ending-structural-racism/unite
14. 1. Oliveira DFM
    2. Ma Y
    3. Woodruff TK
    4. Uzzi B

    (2019) Comparison of national institutes of health grant amounts to First-Time male and female principal investigators

    Jama 321:898–900.

    https://doi.org/10.1001/jama.2018.21944

    • PubMed
    • Google Scholar
15. Software

    1. ORWH

    (2021) Career Development & Interprofessional Education Office of Research on Women’s Health

    Career Development & Interprofessional Education Office of Research on Women’s Health.

    https://orwh.od.nih.gov/career-development-education
16. 1. Peifer M

    (2017) The argument for diversifying the NIH grant portfolio

    Molecular Biology of the Cell 28:2935–2940.

    https://doi.org/10.1091/mbc.e17-07-0462

    • PubMed
    • Google Scholar
17. Software

    1. Saez E

    (2021) WID - World Inequality Database

    WID.WORLD.

    https://wid.world/
18. 1. Saez E
    2. Zucman G

    (2020) The rise of income and wealth inequality in America: evidence from distributional macroeconomic accounts

    Journal of Economic Perspectives 34:3–26.

    https://doi.org/10.1257/jep.34.4.3

    • Google Scholar
19. 1. Sinatra R
    2. Wang D
    3. Deville P
    4. Song C
    5. Barabási AL

    (2016) Quantifying the evolution of individual scientific impact

    Science 354:aaf5239.

    https://doi.org/10.1126/science.aaf5239

    • PubMed
    • Google Scholar

Author details

1. Michael S Lauer

   National Institutes of Health, Office of the Director, Bethesda, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Methodology, Writing - original draft

   For correspondence

   Michael.Lauer@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9217-8177

2. Deepshikha Roychowdhury

   NIH Office of Extramural Research, Bethesda, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

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