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Everyone in #academia knows the struggle of getting a #grant through peer review – but despite how crucial it is to doing research, there’s very little research on how grant applications get funded!
Today we’ll highlight what is known so far.
#academicchatter
Today we’ll highlight what is known so far.
#academicchatter
We know that the funds aren’t distributed equally – @yardenkatz and @umatterdata found that not only do the top 10% of NIH grantees get 40% of the grants, but the ratio has been getting worse over the years. https://www.tandfonline.com/doi/abs/10.1080/09505431.2019.1694882
It’s not just NIH, the UK’s EPSRC has 8% of grantees getting 50% of the grants, as found by @lincongnito https://www.pnas.org/content/112/48/14760
The paper we featured by @EmilBargmann & @AagaardKaare found that the top 20% of grantees got 50% of the grants in Denmark https://osf.io/preprints/socarxiv/j874c/
The paper we featured by @EmilBargmann & @AagaardKaare found that the top 20% of grantees got 50% of the grants in Denmark https://osf.io/preprints/socarxiv/j874c/
There’s not just a bias towards individuals, but a bias towards top institutions. Wayne Wahls found that prestigious institutions have not only a 65% higher grant success rate, but also 50% larger awards. https://www.biorxiv.org/content/10.1101/367847v1
It’s easy to think that the bias towards prestigious is because they’re more productive, but Wahls actually found that lower prestige Unis publish more!
Instead the network that the Uni provides can explain that difference, as found by Val Burris: https://journals.sagepub.com/doi/abs/10.1177/000312240406900205?casa_token=AtPPUZcHvGAAAAAA%3AQXhI6LLu9bvqfr992yy3t3hxATbURAiIXbyX3zL1qeRFL6r6fokgvWvnbY7ph128wjmquRzTJqL7dw&
Instead the network that the Uni provides can explain that difference, as found by Val Burris: https://journals.sagepub.com/doi/abs/10.1177/000312240406900205?casa_token=AtPPUZcHvGAAAAAA%3AQXhI6LLu9bvqfr992yy3t3hxATbURAiIXbyX3zL1qeRFL6r6fokgvWvnbY7ph128wjmquRzTJqL7dw&
When we look at the individual applicants, no one is particularly surprised to see that there is a gender, ethnic, (and other) penalties to grant success.
First up, the most well studied of the disparities: gender. #WomeninScience
First up, the most well studied of the disparities: gender. #WomeninScience
@MLerchenmueller and @OSorenson quantified the ‘leaky pipeline’ when it comes to women in STEM – they transition from doctoral programs to professorship 20% less often than men. (And only 60% of that because of productivity differences) https://ideas.repec.org/a/eee/respol/v47y2018i6p1007-1017.html
@GemmaDerrick also found that there is a clear, gendered perception of ‘impact’ that can affect the grant peer review process, penalizing women. #WomeninSTEM https://www.nature.com/articles/s41599-020-0438-z
The faculty from those prestigious institutions that get the most funding also have a habit of hiring less women, as @JSheltzer confirmed. https://www.pnas.org/content/111/28/10107
Also, those labs lead to higher career outcomes, as found by @sheadwo and @jeremyfreese. https://academic.oup.com/sf/article-abstract/94/3/1257/2461975
Also, those labs lead to higher career outcomes, as found by @sheadwo and @jeremyfreese. https://academic.oup.com/sf/article-abstract/94/3/1257/2461975
Racial barriers to funding are a little harder to uncover – not because they’re not there, but because there’s not enough data. Grant documents aren’t publicly available so we can only know that #minoritiesinSTEM succeed less often, not why.
@UKRI noted that their minority PIs were succeeding less often than white (same for women v. man), but they mention that the sample was too small to have reliable stats. Discrimination is hurting minorities so much we can’t even study them! https://drive.google.com/file/d/18hMHKhZX5UIM9RKNvHj55G2iNH0QJDIV/view
The US has the same problem, with the NIH less likely to fund African-American R01 awards than white. Donna Ginther, Shulamit Kahn and @waltertschaffer were some of the first to gain access to NIH data to show the extent of the problem. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965301/
George Santangelo et al, having access to essentially all NIH proposals, found that topic choice accounts for 20% of the gap between black and white success rates (controlling for other factors)
But there’s still 80% to go! https://advances.sciencemag.org/content/5/10/eaaw7238
But there’s still 80% to go! https://advances.sciencemag.org/content/5/10/eaaw7238
We’ve tried to tackle this ourselves – but we found that our data set doesn’t contain enough grants from #minoritiesinSTEM to get very far. Plus, data sets are always collected from the perspective of the funding agency, not the individual.
We want to collect data and is from the individual grantee’s perspective- to see what the funder-side data has overlooked.
If you want to help us see exactly how deep disparities go, submit your grants to our repository here:
https://proposalanalytics.org/proposal-submission/
If you want to help us see exactly how deep disparities go, submit your grants to our repository here:
https://proposalanalytics.org/proposal-submission/
