We earliest matched near misses and slim gains when it comes to numerous observable attributes (look for Steps section for additional malfunction out-of CEM), and acquire whenever matching, near misses nonetheless outperformed thin wins with regards to each other hit documents (sixteen
The performance advantage of the near misses is particularly surprising given that the narrow wins, by construction, had an initial NIH funding advantage immediately after treatment. Given that funding from the NIH can be an important means to augmenting scientific production 35,38,39,52,56,57,58 , we investigate funding dynamics for the near-miss and narrow-win groups over the following ten-year period. We find that the near-miss group naturally received significantly less NIH funding in the first five years following treatment, averaging $0.29 million less per person (Fig. 2d, t-test p-value < 0.05, Cohen's d = 0.28), which is consistent with prior studies 8,52,59 . 2d, t-test p-value > 0.1, Cohen’s d = 0.02). Although the NIH is the world’s largest funder for biomedical research, near misses might have obtained more funding elsewhere (see ‘Additional funding by near misses’ in Supplementary Note 3 for details). To test this hypothesis, we further collected individual grant histories for PIs in our sample from the Dimensions data, allowing us to calculate the total funding support from agencies worldwide beyond NIH. We first measured the total funding support from the U.S. National Science Foundation (NSF) received by individuals with the same name in the same period, finding narrow wins obtained significantly more NSF funding within 5 years after treatment. We further calculated the total funding support from agencies other than the NIH or https://datingranking.net/nl/elite-singles-overzicht/ NSF, finding that near misses did not acquire more funding than narrow wins. We also manually checked acknowledgment statements within a fraction of papers published by the two groups, finding again the same conclusion.
The fresh RD approach helps us eliminate unobserved has an effect on towards the investment lead otherwise people otherwise unobserved private attributes you to differ efficiently that have the latest get 63,64 , allowing us to next present a great causal outcomes of early-occupation near-miss and you will future occupation effect
Together, such show demonstrate that over the course of ten years, close misses had less initially offers from the NIH and you will NSF. Yet it sooner or later penned as much records and you may, extremely contrary to popular belief, brought works one garnered dramatically high has an effect on than simply the narrow-winnings counterparts.
Is the uncovered difference in outcomes causally attributable to the early-career setback? Or, could it be explained by other alternative forces? Indeed, there might still exist observable or otherwise unobserved factors that affect funding success near the threshold (e.g., individual characteristics 60 , fields of study, personality traits, etc.), which might also drive future career outcomes. To rule out alternative explanations, we leverage two additional inference techniques, Coarsened Exact Matching (CEM) 61,62 and fuzzy Regression Discontinuity (RD) 63,64 . 4% for near misses, 14.0% for narrow wins, ? 2 -test p-value < 0.001, odds ratio = 1.20) and average citations per paper (30.8 for near misses and 27.7 for narrow wins, t-test p-value < 0.001, Cohen's d = 0.05, see ‘Matching strategy and additional results in the RD regression' in Supplementary Note 3 for details). While matching can only eliminate potential observable features, we further mitigate the effect of other observable and unobservable influences using the RD analysis. Specifically, we use an indicator for the score being above or below the funding threshold as an instrumental variable (IV), rather than the actual funding outcome itself, to predict future career outcomes (see Methods section). By accounting for any potential confounding factors, our RD estimates indicate that one early-career near miss increases the probability of publishing a hit paper in the next 10 years by 6.1% (p-value = 0.041), and the average citations per paper by 34% (9.67 citations in 5 years, p-value = 0.046) (see Methods section). The RD analyses help establish the causal interpretation of our results, and the agreement in results across all the methods further demonstrates the robustness of our findings.