To those pursuing a career in the sciences, especially in a research capacity, failure is the norm. However, the long term impacts of repeated failure remain unclear to many early career researchers. On one hand, the so-called Matthew effect claims that early career failure is a negative in the sense that early success can amount to larger future gains, following a similar logic to “a dollar saved today is worth more than a dollar saved tomorrow.” Screening effects offer an alternative to the Matthew effect and suggest that early failures serve to screen out the less dedicated scientists for future career advancement. Either way the cost-benefit analysis of early career failures (whether they be large or small) is a common conversation among colleagues that usually leaves most involved feeling confused or even anxious due to the lack of any firm data on the subject, until now.
A recent study by researchers from Northwestern University used data from the National Institute of Health R01 grant applicant data pool to quantify the effect early career setbacks have on various future career milestones. In this study they defined a “set-back” as not securing an R01 grant and compared two cohorts of junior scientists who were all within three years of starting their independent careers. The “near-miss” cohort represents those whose scores were within 5% of missing funding (n = 623) and the “narrow-win” cohort represents those who were within 5% or less of just making the funding cutoff (n = 561).
|Study Group||R01 Grant Application Result|
|Near-miss||Score within 5% of missing funding|
|Narrow-win||Score within 5% of just making funding cutoff|
The researchers then followed each cohort over the next ten years of their careers and were able to draw a number of interesting conclusions:
1) The near-miss cohort publishes better papers.
The research term defined a “hit paper” as one that was within the top 5% of citations in a given field for that year. Using this term they compared either cohort’s publication record and found that the near-miss cohort had a 16.1% hit paper rate while the narrow-win cohort had a 13.3% hit paper rate. Not only is this difference a statistical significant one for the near-miss cohort (p-value < 0.001) but it is also far above the rate at which all other researchers published hit papers (around 5%). Even more, in the first five years following being turned down for NIH R01 funding the near-miss publications attracted 19.4% more citations than those from the narrow-win cohort.
2) Near-miss scientists ultimately engage in more translational science.
In the ten years following the NIH decision 5% of publications by near-miss scientists were those that reported results of clinical trials using technologies which they had developed – nearly double the rate at which narrow-win scientists were taking their work into the clinic. Additionally, 34% of near-miss publications (as compared to 27% of narrow-win publications) over the same time frame where citied by clinical trial publications which implies that these works had indirectly influenced the trial.
3) There is a higher attrition rate for the near-miss cohort.
Despite an apparent advantage in quality of work published by the near-miss cohort, these scientists suffer from a nearly 13% high attrition rate from the NIH system in the first five years post-decision. While this does suggest the worst, that is these junior scientists did not earn tenure, the authors stated rather nicely that there is “evidence that PhDs who left science are disproportionally employed at large, high-wage establishments.”
So what is the author’s final conclusion? Generally stated they claim that past set-backs can act as reasonable predictors of future success. The mechanism by which this occurs, however, remains unclear. From reading the publication I personally was left with the impression that not securing funding for a particular grant project caused junior faculty to shift their focus to newer, more translatable areas. The higher attrition rate could then be explained by the fact that it is difficult to join in on new scientific band-wagons before they become well-vetted (think gene editing). I encourage readers to take a look at the article and come up with an opinion themselves. Given this data do you think early career setbacks are positive or negative?
Featured Image is Figure 1.c from the open access article discussed herein. Citation:
Wang, Y., Jones, B. F., & Wang, D. (2019). Early-Career Setback and Future Career Impact. SSRN Electronic Journal, (2019), 1–10. https://doi.org/10.2139/ssrn.3353841