Early-career setback and future career impact

Setbacks are an integral part of a scientific career, yet little is known about whether an early-career setback may augment or hamper an individual’s future career impact. Here we examine junior scientists applying for U.S. National Institutes of Health (NIH) R01 grants. By focusing on grant proposals that fell just below and just above the funding threshold, we compare “near-miss” with “near-win” individuals to examine longer-term career outcomes. Our analyses reveal that an early-career near miss has powerful, opposing effects. On one hand, it significantly increases attrition, with one near miss predicting more than a 10% chance of disappearing permanently from the NIH system. Yet, despite an early setback, individuals with near misses systematically outperformed those with near wins in the longer run, as their publications in the next ten years garnered substantially higher impact. We further find that this performance advantage seems to go beyond a screening mechanism, whereby a more selected fraction of near-miss applicants remained than the near winners, suggesting that early-career setback appears to cause a performance improvement among those who persevere. Overall, the findings are consistent with the concept that “what doesn’t kill me makes me stronger.” Whereas science is often viewed as a setting where early success begets future success, our findings unveil an intimate yet previously unknown relationship where early-career setback can become a marker for future achievement, which may have broad implications for identifying, training and nurturing junior scientists whose career will have lasting impact.

That is the abstract of a new paper by Yang Wang, Benjamin F. Jones, and Dashun Wang.

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"Yet, despite an early setback, individuals with near misses systematically outperformed those with near wins in the longer run, as their publications in the next ten years garnered substantially higher impact"

Wouldn't this be consistent with selection bias?

In most competitions with many applicants, the difference between a near-win and a near-loss is close enough to random.

If a portion of the near-losers decide to give up but the near-winners all continue on, then the remaining near-losers will be disproportionately more stubborn and persistent. Wouldn't we then expect them to outperform the intact group of near-winners, even in the absence of any modification of their future behaviour?

End these money wasting grants.

We need to spend more on science not less.

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"We further find that this performance advantage seems to go beyond a screening mechanism..."

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Though the sentence is clunky, they deal with your point to n extent - 'We further find that this performance advantage seems to go beyond a screening mechanism, whereby a more selected fraction of near-miss applicants remained than the near winners, suggesting that early-career setback appears to cause a performance improvement among those who persevere.'

Whether this mechanism applies - 'then the remaining near-losers will be disproportionately more stubborn and persistent' is open to question, since they at least attempt to filter out the effect of the drop outs. It may merely be that the near losers are shown the need to do higher quality work, while the near winners feel they are good enough already.

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Possibly the winners already achieved their goal - of an academic sinecure - and so didn't feel the need to work so hard in the future.

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I wonder if they looked at gender differences. I suspect that women are more likely to perform less well after an initial rejection, due to a greater feeling of "impostor syndrome" or similar ego crushers., and may even leave the field.

Interesting. I suspect you are right.

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Women need to try harder or they will fail as a species.

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It would be consistent with the proposition that the probability of the near-miss proposal being a bit more "out of the box" than the near win would be higher.

I wasn't involved in RO-1 of NIH but was involved in related scientific committees of other government research organizations and I note the risk-averse nature of the committee's giving solid, but very predictable proposals the nod over a proposal with more risk of failure and more reward.

In these government-funded research project proposals, the method of achieving the results are detailed and the chances of achieving the desired results are very high. As the funding is fixed and there is no advantage to killing the project after you get into it, making sure you get useful, if not dramatic, "publications or results" makes sense.

In the private sector doing a research project or even a VC or AI (angel investor) funded project, it makes sense to kill a project when the initial research results make a dramatic change in the success probability. A more Bayesian approach of reevaluation as the research progresses. Even non-statistically significant results (not 95% confidence levels) indicating a significant problem can kill the proposed project and reallocate the funds elsewhere.

The concept of "fail fast and early" and recycling the money into a different research direction doesn't fit with an NIH and other government grant models of buying X years of a well-planned research effort.

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