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Retention Rates for First-Time R01 Awardees

We have seen increased interest in the biomedical workforce by Congress and especially by our community. From our end we’ve in particular observed heightened attention to how the dynamics of the workforce impact researchers in the early stages of their careers. So the topic definitely deserves our continued attention and I thought in light of this it’d be a good time to share some of NIH’s analyses on one specific aspect of this that my office has been closely examining.

We know that over time investigators are, on average, 42 years old when receiving their first R01, which is older than it was than before the doubling of the NIH began. And, we know that funding rates for both first-time NIH investigators and experienced NIH investigators alike have declined. So we wondered, of those first-time recipients of NIH R01-equivalent funding, how many years after their first year of R01 funding to they receive additional research grant funding? And furthermore, given the changes in NIH’s budget over the years, does the year they received their first R01 – and the rise of fall of NIH funding – correlate with whether or not they remained an NIH-supported researcher in subsequent years?

Here is a graph of the amount of funding NIH had available for competing research project grant awards from 1986 through 2013. In blue are the actual dollar costs, and in red are constant dollars, normalized to the value of a dollar in 1986.

NIH Research Project Grant (RPG) Funding Competing Awards, FY 1986-2013

We chose three cohorts of first-time R01-equivalent awardees — those who received their first R01-equivalent award in 1989, 1997, or 2003. We looked at these three time periods because of their relation to the NIH budget when their initial award was coming to end, i.e., what was the budget like when they would need to re-compete. For the cohort with an initial award in 1989, four years later (1993), the NIH budget took a dip with little to no growth for a few years. For the cohort with an initial award in 1997, four years later (2001) the NIH budget was in the midst of doubling. Finally, for the cohort with an initial award in 2003, four years later (2007) the NIH budget was not growing and was actually losing purchasing power.

First, let’s look at how these cohorts compare:

We used data on these three cohorts for a Kaplan-Meier analysis to look at rates of retention. Kaplan-Meier is a type of statistical analysis used to determine the likelihood that a specific event will occur (in this case, dropping out of the RPG funding pool) over the course of time. So we used this to analyzing the number of years between the first year of R01-equivalent funding, and the last time an individual receives any additional research project grant (RPG) funding – whether it be from the non-competing continuation of their 1st R01 or another RPG award.

Retention of first-time NIH R01-equivalent recipients in the NIH funding pool Select cohorts

These retention curves tell us that for all three cohorts many PIs stop receiving NIH RPG funding about three to five years after they receive their first year of R01 funding. For the 1989 cohort, PIs drop out of the NIH RPG pool most markedly at years 3 and 5, with a sharp drop after year 5 followed by a steady slow subsequent decline. For the 1997 cohort, the drop between years 5 and 6 was much less precipitous, indicating that more PIs in this cohort were able to maintain competitive funding. This is not surprising given that they were re-competing during the NIH budget doubling. Indeed, 12 years later, over half of the PIs in the 1997 cohort still had RPG funding.

Now, let’s look at the 2003 cohort. Again we see a drop between years 4 to 6, but it is not as severe as the 1989 cohort, yet not as buoyant as the 1997 cohort. As we complete the award data for FY 14, it will be interesting to see if the 2003 cohort continues to drop more sharply (because of the recent NIH budget) or if it moves into a more steady and slow decline similar to the earlier cohorts.

This of course leads to the question – do these PIs disappear from NIH’s pool of RPG awardees because they never came back to apply for another grant? So, we performed a similar analysis with these cohorts, following the individuals through FY2013 but this time looking at the last year they submitted an RPG and drop out of the RPG applicant pool.

Retention of first-time NIH R01-equivalent recipients in the NIH applicant pool Select cohorts

Interestingly, the 1989 cohort didn’t return for funding persistently after their first R01, but the 1997 and 2003 cohorts did continue to submit RPG applications. While we can’t use this data alone to determine why the 1989 cohort dropped out of the applicant pool at a faster rate than the ’97 and 2003 cohorts, it is interesting to note the difference between the 1989 and later cohorts.

Looking at these three cohorts, there are a couple of take-away messages. First, a significant number of first time PIs appear to drop out of RPG funding status following their initial award regardless of the economic times; all three cohorts had dropped between 30 to nearly 50% within 5 years of the initial award. For the 1989 cohort, the drop out closely tracked with their lack of persistence in submitting application whereas both the 1997 and 2003 cohorts showed a more gradual decline in submitting RPGs. Second, the rate of drop out corresponds to the NIH budget climate at the time of re-competition with more PIs staying in the system during the period of the doubling than the period of time prior to the doubling. Although it is too soon to see the longer term retention of the 2003 cohort, the data to-date suggest that their retention is worse than those who recompeted during the doubling, yet slightly better than those who were recompeting in the late 90’s, before the NIH doubling. Time will tell if this “between the extremes” status continues into the future.

These data seem to support the concept that if there is an intervention needed in retaining scientists in research, it would need to come at the renewal stage of the first award, or as some call it the “second” award. Indeed, we are giving increased focus to this stage through some of our new award mechanisms, such as the National Cancer Institute’s Outstanding Investigator award, and will continue to seek ways of keeping our talent from leaking out of the pipeline.

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42 thoughts on “Retention Rates for First-Time R01 Awardees

  1. The alarming fact is that close to 50% of the first time awardees are unable to compete for grants again. This tells us one of two things, either we are funding too many of the wrong people, or it is so much harder to get funded without the new investigator bump. Both of these are probably true. So, what this means is that we need to find a better way of predicting who is going to be able to compete for the second round. This means we need to draw back on our obsession with technical innovation and focus more on the individual’s past performance and potential for advancing the field. We also need to throw out the way we fund our first time investigators. Something other than simply a 5-10 point bump in the pay line – clearly that system does not work. A 50% grade is failure at most schools.

      • There are several differences between payline (which is only roughly the fraction of current-round applications funded), success rate (fraction of applications eventually funded either as initial or revised submissions), and retention rate (investigators who still have funding, irrespective of whether it is through the same grant or not). You do not stay or leave on one application. To stay in the pool, you need more than one idea and a willingness to keep trying. Look at it this way: if your hypothetical PI’s applications have a 14% chance of funding, you would hit 54% retention at four applications/investigator.

        The real inefficiency is that the current level of uncertainty in funding leads to more time spent drafting (and reviewing, and responding to reviews of) applications rather than doing science. It also leads to more cautious, less ambitious, and less creative science–and that is not easily quantified.

    • David P. is correct about the ineffectiveness of the new investigator discount. It actually is compounded by study section behavior that additionally factor in the discount when scoring the grants (this is often stated in the discussion), so that these investigators are actually receiving a double discount. I suspect when the next cohort is analyzed, the drop off will be even steeper. Based on this data, it seems hard to justify this approach when funds are being shunted away from established investigators.

  2. What’s missing from these data is an appropriate baseline for comparison. Presumably some non-negligible proportion of PIs also drop out after their 2nd, 3rd or 4th R01. The question of interest here is whether the drop-out rate after the 1st award differs meaningfully from the drop-out rate after a later award. The post seems to imply that this is the case, but that actually isn’t really demonstrated by the above analyses. The last two figures are somewhat suggestive, inasmuch as there’s a sharper break around 4 and 5 years, but this is actually to be expected, because as time goes on, the timing of awards ending becomes progressively smoother (because of different start dates and durations of each award). Could you please plot or report (a) the proportion of PIs whose 2nd, 3rd and 4th awards ended during the same time periods (i.e., 1989, 1997, and 2003), and/or (b) the proportion of these cohorts who dropped out after the 2nd R01? Those numbers would provide some context and give us all a better sense of how much worse (if at all) the drop-out rate is after the first award relative to later awards.

    (It’s also worth noting that there are other reasons why one might expect the drop-out rate to be higher after the 1st award than subsequent awards that have no meaningful implications for NIH policy. For example, some proportion of new PIs likely leave academia after failing to get tenure; the same is probably true to a much lesser extent of PIs with multiple R01s, who are much more likely to already have tenure, and hence are unlikely to lose their positions due to factors that have little or nothing to do with NIH.)

    • I am sure that Tal is correct that some of those not getting the second award do not get tenure and then drop out. However, this is a chicken and egg question. Did they not get tenure because they could not get another grant? I know from my own university that this is quite often the case. Tenure requires the ability to get more than one grant at most institutions. The ease of getting the first grant compared to the second grant is not lost on P&T committees. Unfortunately, we have too many “one and done” investigators, but the question is whether this is the fault of the applicant or the way in which we fund investigators. Probably some of both, but the latter is something NIH can change.

  3. Why is the average age at first R01 going up? Given the extremely intensive technological prowess required to carry out studies on complex biological problems, a graduate education may last 6-7 years post-BS. The need to then acquire advanced expertise and a research direction through post-doctoral studies may require 2 such post-docs (3-6 years) before independence. Getting the first R01 may also take 2-4 years, if the academic institution has the patience to wait that long. Thus, a period of 8-17 years may be needed post-BS to get to the first R01. Thus it is no surprise that the average age at first R01 is ~40-42.

  4. Can somebody explain to me how the National Cancer Institute’s Outstanding Investigator award is going do anything for fellows applying for the renewal of their first award? I think that there is some lack of connection between NIH administrators and the real world.
    Not that I think that there should be any special treatment for fellows who got 5 years of funding to prove their ideas, demonstrate their talent and generate data for a new application. That would prevent other projects (including those from new investigators) from being funded. Peer-review, competition and let’s fund the best project.

  5. What is the breakdown by the sex of the PI?any differences we should be aware of?

    Also, how has the proportion of 3, 4 and 5 year R01 awards to NIs changed over time?

  6. I would very much like to see an analysis of gender/race in these cohorts. Who or what are the characteristics of the first-time awardees who leave (likewise, those who stay)? It’s possible that these factors have nothing to do with it, but it would be good know, especially if there is an interest in addressing the problem at this career stage.

    • We agree that there are several workforce demographics that deserve further investigation, and we’ll be looking at these data further.

  7. Less than 16% of Biomedical PhD’s are obtaining tenure track faculty positions 5-6 years after earning their PhDs. Some proportion of those that do will not be awarded even their first R01 and will likely lose their position. 30-50% of those that do receive their first R01 will not continue to be awarded RPGs. So does this mean that somewhere in the order of 5-10% of those earning a biomedical PhD will make it as a PI? I think one can easily see why grad students and postdocs are trying to (or having to) exit this career path in increasingly growing numbers.

  8. I don’t understand exactly what problem we are supposed to see with these data. There’s a noticeable attrition rate for principal investigators in the NIH system that has improved over the past 20 years, but doesn’t seem to have changed over the past 10 to 15. There’s not been an increased attrition, so what is new or interesting that would require a shift in policy? Is this attrition rate (which has declined) suboptimal? If so, why? If it was okay for 30+ years, why is this a problem now, given the other issues facing PIs (and Program) in the system? As far as I’ve seen, success rate for type 2 applications has remained significantly smoother over the years than type 1 for both NI/ESI and Established investigators, so I don’t understand if this post is supposed to be highlighting the emergence of a new trend, while the data seem to be indicating that nothing much has changed. Perhaps that the career paths of PIs since the 90s rely more on NIH funds than they previously did.

  9. I have been a department chair for 20+ years and recruited 27 tenure/tenure track faculty. All 27 achieved (or came with) NIH R01 funding. Of these four did not achieve tenure two men-two women. The common denominator in all four cases was failure to establish and maintain a laboratory sufficiently productive to achieve renewal of the initial award or additional grants.

    All were provided a proactive mentoring/professional development committee from day one and received written annual reviews from their peers (department PT committee) and, independently, from the chair. Reviews included suggestions for reducing teaching or providing additional resources (which were universally followed) as well as identifying shortfalls in productivity benchmarks. Sadly, in four cases this was of no avail.

    Not every hope is fulfilled. Tweaking NIH funding policies is unlikely to change this.

    • When were these individuals hired? If they were hired, was it within the last 6 to 8 years, I imagine so, what that tells you that the system has severely changed. The bar at many institutions has become impossible to achieve, leaving junior faculty that aren’t connected hanging by a thread, regardless of mentoring, etc. I agree there are other circumstances that affect an individuals ability to achieve tenure. I don’t know your department or institution, so I can’t comment about your specific experience, but its becoming increasingly impossible to achieve tenure in this environment. I know many people who have been fired or left before that happened. The basic problem as I see it is that they aren’t connected (let’s face it its like a mafia for grants, papers, etc.), and this greatly impacts their ability to be successful. Unfortunately, grants aren’t judged on scientific merit, but instead on intangibles, ..is this person a player in the field?, are they connected?, are they a friend?, etc. will they scratch my back?. No one is taught this in grad school or as a post-doc. Basically, academia is loaded with people who are becoming increasingly more aggressive and focused on getting grants, and not on the science! Lots of faking data, and suspect work is arising, along with the new elephant in the room, the predatory journals/publishers which love self-aggrandization and promotion for lifting one’s career forward. These publications are garbage but study sections and panels look the other way. Its become very sad and pathetic, biomedical science is lost in an abyss, and needs to be revamped!

  10. I think the most critical problem for the 1st R01 receivers for renewal are their low competitiveness compared to far more established 3rd, 4th or multiple R01 receivers. When the funding rates are so low, reputation is becoming a very important factor in funding consideration. However, the 1st R01 receivers just do not have sufficient time and experience to get such reputation at renewal. They have not yet achieved an equal level with the senior investigators, though they are considered as “senior investigators” at renewal. I do believe continued support similar to the new investigator is needed.

  11. If my memory is correct, the PIs in these three cohorts did not receive the same “new investigator” or “early stage investigator” treatment as the new PIs today. The “new investigator” designation appeared in NIH grant application forms after 2003. It will be interesting to compare the periods after 2003 to those before.

  12. Let’s not confuse the issues that need to addressed. Yes, the trend of increasing age of the first-time R01 awardees (39 in 1989 and 42 in 2003) needs to be addressed. But, the retention rate (50-70% remain active in the NIH grant applicant pool after 5 years of their first R01 award for the three selected year-cohhorts) seems fine and, I suppose, higher than the success rate of start-up companie in the silicon valley. It is part of selection process for a capitalism-styled funding system that the US has, and the current NIH/NSF system will be even less sustainable if there is a higher retention rate for first-time R01 grant awardees.

  13. I completely agree with mts, both as a long time NIH grant reviewer and faculty member. The thing is, the first grant is largely awarded on “promise” to be productive since the applicant probably only has accomplishments to date achieved under another investigator’s direction (grad school/post doc mentor). The first RO1 renewal is based on the investigator’s own productivity ie they are not getting a second grant if they do not accomplish anything with the first one. The thing is, while no productivity as a trainee is a good predictor of no productivity as a faculty member, many people can not maintain their productivity when no one is on them to do the hard work of publishing when the deadlines to do so are so soft. Some folks also have issues with balancing the competing duties of a faculty member with their research agenda and the research agenda does not get down. However, they were productive as a postdoc as they had the luxury of only focusing on research instead of also having to teach, review papers/grants, do university service, organize conferences, mentor students etc.

  14. These are interesting data, but it is difficult to properly interpret them without knowing the real denominator. That is, the selectivity of the funded cohort in 2003, etc. was likely MUCH greater than the selectivity of the 1989 cohort. Recall that all these individuals were successful in getting their first grants; the NIH made an investment but they may or may not have been able to achieve their work successfully. Many others from the same era were not successful at all, but given declining payline rates over time and an increasing number of competitors (as we’ve trained more and more scientists), the number of applicants relative to successful first time grant recipients has probably increased markedly across these 3 cohorts. Finally, each successive cohort has had more competition from the senior scientists who write multiple grants to keep their labs growing, suggesting that a larger share of funding is increasingly concentrated in the hands of more senior investigators. Given growth in the population of grant applicants, relative stability of real dollars available, the growing cost of “big science” across all fields and the long lived careers of major laboratory leaders these data are not surprising. The challenge is to properly diagnose what is happening and to develop a strategy the further maximizes the value of our investment in science for our society.

    • Vince-

      1) the fixed size of the modular award combined with decreasing purchasing power means PIs require something like 1.8+ R01s to equal the single R01 of 15 yrs ago.

      2) this blog posted data on the change in number of grants per PI a few years ago and it didn’t really support your claim of increased concentration of grants with the few (beyond the issue raised above).

  15. It appears that many of the respondents above would like to see additional analyses of the data. I am wondering why the data can’t be made publicly available. Of course this would require removal of individual identifiers but this seems feasible for this relatively small dataset of about 1800 NIH R01 awardees each year. I am sure we could learn much more about this issue with numerous data analysis experts (scientists) working with the raw data.

  16. I am curious what dropoff rate the NIH administrators would expect to see if we were not in a funding crisis. If a first R01 is an investment in a new PI’s future, we should expect a certain failure rate since not everyone who seems cut out for independent research will necessarily be good at running their own lab. Is 10% reasonable? 20%? 40%? It is important to define the problem before contemplating a solution.

  17. The difference shown in the last graph, between the 1989 cohort and the later two, should be expected given the increase in soft money positions over the past 30 years — if you hold a soft money position, you have to keep applying. During these decades, teaching has been devalued and research support has moved away from those who also teach (i.e., interact more with students). These considerations lead me to view this graph more negatively than others might.

  18. It would be interesting to stratify the data according to job title, i.e. tenure track, tenured faculty, investigators at private institutions with faculty status, and those in non-tenure earning/non-faculty positions.

  19. So now we are going to give first AND second applications special dispensation? How about special dispensation for people submitting their fourth renewals? We could use some help too. The established investigator pool is pretty rough no matter where you fall in the spectrum. Why would it be worse to drop out on your second R01 than your fourth? Mid career science is usually better than early stages, why should better science drop out? It makes no sense.

  20. This is interesting data, thanks. It is valuable to look at the numbers. Overall, though, this idea of looking at various aspects of funding separately seems shortsighted. First, it was “age of first time awardees is getting too high. We need to fund more young investigators.”. While this seems like a nobel thought, the obvious problem is that simply getting more people _started_ in not enough if you can’t sustain them. What happens after their first award? If the total pot of money is fixed, shifting money towards certain kinds of investigators would necessarily hurt other kinds of investigators. If you shift money towards funding younger people, then the non-younger-people would have to suffer. Predictably, now it is “lot of people are dropping out after their first award. What can we do to keep the talent from being leaked away?” Seems like a “duh” question. Increase the total pot (seems unlikely) or shift more money to whichever group you want to help (thereby hurting other groups).

    Look at all the numbers and decide a long-term strategy. What are the first, second, third, fourth time investigators that can be funded and sustained? You can’t do it in a constant “putting out fires” mode. OMG, look at these people, they really need help, let’s start programs to help them. OMG, look at these other people, they need help, let’s start these other programs. OMG…

  21. How about a much simpler question? How many PIs that have had an NIH grant have been dropped from the NIH funding pool? I would expect a dramatic difference for this given the current funding climate….

  22. A further interesting analysis would be the retention of K-awardees to actually submit or receive a R01 (or equivalent awards) as well as the breakdown between MDs, MD/PhDs, and PhDs. This has not been performed for many years.

    NIA data suggest MDs are disappearing rapidly, with a decrease in K08 applications by MDs from 40% of the pool in 2002 to 5% of the pool in 2013.

    Physician-scientists are becoming an endangered species as the prospects of research careers are so bleak, that no one with any common sense wants to pursue a research career.

  23. I’d like to see the data for clinician scientists. There is a significant barrier in getting the first RO1, but the continuing challenges of maintaining a funded research program probably differ among clinician and non-clinician researchers. Those differences may be reflected in divergent patters in these data sets.

  24. Someone will correct me if I am wrong, but wasn’t the mid-early 90’s a period of rapid expansion in big pharma offering alternative options for those that were discouraged by the outlook at NIH? How persistent someone is with reapplications has a lot to do with what other options are available.

  25. I feel incredibly fortunate to have had the opportunity to apply for RO1 funding and I appreciate all the work the NIH is doing to help investigators pursue their research interests. While I was never awarded a RO1, I did see improvement in scores over the years and saw a glimmer of hope near the end. Unfortunately, I was at a small college, with few resources, and this was mentioned in almost every review. I wonder what these data would look like for faculty who are at small colleges (with little or no history of RO1 funding) when compared to larger, more established research institutions. I bet there is a stark difference.

  26. I do not understand why success rate is calculated based upon # of reviewed proposals as opposed to number of received proposals. As competition increases, so does the “triage” rate. Why not publish success rate as number of grants funded divided by number of grants submitted? That would paint a clearer picture of success / failure. I would second the call for a release of the data so that independent highly detailed analyses can be performed.

  27. I agree with many of the sentiments expressed above. Before making too much of the dropoff in refunding, more analysis is needed. Also, I am not sure that it should even be a concern that a substantial number of people do not get refunded after their first R01s run out. This is often the first time that a young scientist is responsible for all aspects of a project, including hiring and supervising people, as well as making sure that he/she continues to get papers published, while at the same time taking on significant teaching and administrative loads. Junior faculty are hired on the basis of potential, but often there is no information about ability to manage people (including themselves). Some are not quick learners when they first jump into the management aspect of science, and some also refuse to take input from their more senior colleagues to help them succeed at these tasks. If NIH is concerned that the loss of these people to the review system means that a lot of the initial investment on these people is wasted, maybe there are some other options to consider. These might include a lower cap on the amount of direct costs that a junior investigator can ask for in an R01, to give them time to grow their supervisory skills.

    • The ‘lower cap’ idea was tried in the late ’80s. It was called an R29. These grants were virtually the kiss of death. Not enough money and too much expectations (50% effort & salary). The real problems are too many people in the system with insufficient funds available to support their research, and a study section system that is overloaded leading to inadequately prepared reviewers with the majority of the study section not being able to read the grants and yet they still vote based on the ‘discussion’. A single negative view, even if not based in fact, can put a grant down or even keep it from being discussed.

  28. Food for thought is that purchasing power is probably further compromised if you consider the cost of incorporating new technologies and the increase in the scope of projects to remain competitive (both in grants and in publications). I am not sure that inflation adjusted dollars are a good metric as inflation in science is driven by very different forces than inflation on the street. An interesting analysis would be assessing the actual operating costs per lab of established departments in year 2000, 2005, 20010 and 2015. Also worth evaluating the evolution of the cost associated with the preparation of a new proposal. The amount of money (not to mention time and effort) that is funneled into generating data purely aimed at supporting new grant applications & resubmissions may be a factor further eroding the operating capacity of many labs.

  29. I’m confused about the meaning year “0” in these plots. The text implies that it is year 1 of 1st grant, BUT 2003 + 6, 1997 + 20, 1989 + 20 all = 2009, not 2013.

    Whatever year 6 means, it looks like the 2003 cohort is less successful at staying funded than the 1997 cohort, but they also are not dropping out as fast. Fewer viable alternative career options? More soft money positions? Both?

    This change also argues that there is something odd about the statistic showing a similar overall success rate for R01s. Clearly the 2003s ARE experiencing less success at whatever it is they’re applying for.

  30. I found the data particularly interesting because I am part of one of these cohorts that dropped out of the NIH RPG applicant pool. Based on my experience, and my time served on NIH study sections, I would add the following observations:

    1. For any given investigator, dropping out of the applicant pool is a long, slow, and frustrating process. This usually involves multiple rejections over several years. Given the immense time spent writing each proposal, one eventually comes to the conclusion that it’s more worthwhile to change research focus and find funding from other sources, rather than chase the elusive renewal that seems less likely each year. It would be interesting to know what is the average number of unfunded submissions before an investigator drops out of the system, particularly for mid-career investigators that have tenure and established research labs. Should the NIH explore ways to bring some of these investigators back into the system, as the loss of their expertise and scientific contributions surely hurts the biomedical research community.

    2. Rather than just funding changes, there have also been NIH policy changes that have negatively affected many investigators. The shake-up of the study sections left many fields without representation among the remaining study sections, and this results in many proposals that are reviewed by individuals without sufficient expertise to judge the merits of the proposed research. More recently, the focus on translational research has made it increasingly difficult for investigators who focus on fundamental scientific questions to compete for NIH funding, regardless of the potential value of the results.

    3. Based on my observations serving on study sections, many of the investigators who remain funded for decades usually have two or more NIH grants that cover closely related projects, often with overlap that creates some synergy. The investigators are able to use multiple grants to generate a larger number of publications, which at renewal time they attribute whichever funding source is up for renewal; they are able to make it appear that a single 4-year grant generated 20-30 publications, when in fact these publications are funded by multiple grants. It would also be interesting to see the differences in retention rate for investigators who have 1 vs. 2, 3 or more NIH grants on which they are PIs or Co-PIs. Here I think you need to include all R, K, and P awards to account for multiple overlapping funding sources.

  31. Part of the distinction between the funded and applicant pool, I hypothesize, comes from the changing relative timing of first R01 and the tenure clock. I was hired as an Assistant Professor in 2002. I secured my first (five-year) R01 in 2005, meaning that it was not up for renewal until after my tenure review. At the time this was almost unheard of; the expectation at my institution for many years had been that you would get funded and then renewed in order to qualify for tenure, because P.I.s who were good enough to get an R01 would get it within a year or two of starting. In other words, it used to be that if you were unproductive in your first R01, you would both have trouble getting it renewed and wind up not getting tenure, thus dropping out of the applicant pool. Nowadays, if you get your first R01 3-4 years after starting, you’re still funded when you come up for tenure, and will be granted tenure (assuming you’ve been reasonably productive), and likely remain in the applicant pool for an extended period, even if that first grant is ultimately not renewed.

    I’m not claiming any of this is a good or a bad thing, just trying to explain the data.

  32. With the tightening funding environment and absence of an age limit on PIs, it should come as no surprise that the proportion of PIs who are 66 or older is double that of PIs age 36 or younger, based on a 2012 Rock Talk post (http://nexus.od.nih.gov/all/2012/02/13/age-distribution-of-nih-principal-investigators-and-medical-school-faculty/). Funding rates and retention rates of first-time R01 awardees would likely improve if NIH introduced an age limit for PIs. That would enable well-established investigators to continue their work and require them to share the credit with their junior colleagues.

  33. Matt here is a good NY Times article on the topic for you: “Young, Brilliant and Underfunded” Oct 2, 2014. The key to improving the lot for young investigators is to eliminate cheap foreign post-docs or at least under-trained individuals. An established scientist, holding major grants, can hire these individuals and diminish the value and pay, and competitiveness, of a well-trained and original scientist. It may not be such a big problem now, but it left a wake of ethical problems for an entire generation-and may last much longer since only 30% re-patriated and they may not ever be treated the same (see “Stay Rates of Foreign Doctorate Recipients from U.S. Universities”, 2011, by M. G. Finn, and references to NSF post-doc data therein). The big loss for society is the loss of young original scientists, presumably like yourself,-not adequately supported from the start or through their careers. Sure your germinal idea and data may get published–but you may not be able to compete in this environment.

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