0, so that individual scientists cannot precisely manipulate the score to be above or below the threshold. This assumption is valid in our setting, because the scores are given by external reviewers, and cannot be determined precisely by the applicants. To offer quantitative support for the validity of our approach, we run the McCrary test 80 to check if there is any density discontinuity of the running variable near the cutoff, and find that the running variable does not show significant density discontinuity at the cutoff (bias = ?0.11, and the standard error = 0.076).
Along with her, these types of abilities verify the key assumptions of your fuzzy RD strategy
To understand the effect of an early-career near miss using this approach, we first calculate the effect of near misses for eharmony online active PIs. Using the sample whose scores fell within ?5 and 5 points of the funding threshold, we find that a single near miss increased the probability to publish a hit paper by 6.1% in the next 10 years (Supplementary Fig. 7a), which is statistically significant (p-value < 0.05). The average citations gained by the near-miss group is 9.67 more than the narrow-win group (Supplementary Fig. 7b, p-value < 0.05). By focusing on the number of hit papers in the next 10 years after treatment, we again find significant difference: near-miss applicants publish 3.6 more hit papers compared with narrow-win applicants (Supplementary Fig. 7c, p-value 0.098). All these results are consistent with when we expand the sample size to incorporate wider score bands and control for the running variable (Supplementary Fig. 7a-c).
In regards to our attempt of testing mechanism, we use a conventional elimination strategy since the discussed in the primary text message (Fig. 3b) and you will redo the entire regression study. I get well once more a critical effect of very early-field problem on possibilities to share strike files and you will mediocre citations (Additional Fig. 7d, e). To own hits for each and every capita, we find the end result of the same assistance, while the unimportant differences are most likely because of a lower life expectancy test dimensions, offering suggestive research to the effect (Additional Fig. 7f). Finally, so you can take to the fresh robustness of your own regression abilities, we after that managed most other covariates and additionally guide seasons, PI gender, PI competition, establishment profile because the mentioned of the amount of successful R01 awards in the same months, and you will PIs’ past NIH feel. I recovered a comparable overall performance (Additional Fig. 17).
Coarsened exact matching
To help expand get rid of the effectation of observable facts and you will combine brand new robustness of the efficiency, we employed the official-of-ways means, we.elizabeth., Coarsened Appropriate Matching (CEM) 61 . This new coordinating approach next assures the fresh resemblance anywhere between slim victories and you can close misses old boyfriend ante. Brand new CEM formula involves about three strategies:
Prune about analysis put the newest systems in just about any stratum that do not tend to be at least one managed and one control equipment.
Following the algorithm, we use a set of ex ante features to control for individual grant experiences, scientific achievements, demographic features, and academic environments; these features include the number of prior R01 applications, number of hit papers published within three years prior to treatment, PI gender, ethnicity, reputation of the applicant’ institution as matching covariates. In total, we matched 475 of near misses out of 623; and among all 561 narrow wins, we can match 453. We then repeated our analyses by comparing career outcomes of matched near misses and narrow wins in the subsequent ten-year period after the treatment. We find near misses have 16.4% chances to publish hit papers, while for narrow wins this number is 14.0% (? 2 -test p-value < 0.001, odds ratio = 1.20, Supplementary Fig. 21a). For the average citations within 5 years after publication, we find near misses outperform narrow wins by a factor of 10.0% (30.8 for near misses and 27.7 for narrow wins, t-test p-value < 0.001, Cohen's d = 0.05, Supplementary Fig. 21b). Also, there is no statistical significant difference between near misses and narrow wins in terms of number of publications. Finally, the results are robust after conducting the conservative removal (‘Matching strategy and additional results in the RD regression' in Supplementary Note 3, Supplementary Fig. 21d-f).