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 jdate 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).
Together, this type of show confirm an important assumptions of blurry RD method
To understand the effect of an early-career near miss using this approach, we first calculate the effect of near misses for 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).
For our take to of evaluation method, i utilize an old-fashioned reduction approach just like the demonstrated in the main text message (Fig. 3b) and you will redo the complete regression analysis. I recover once more a life threatening aftereffect of very early-occupation setback into the probability to share hit paperwork and average citations (Supplementary Fig. 7d, e). Getting hits each capita, we find the end result of the same assistance, therefore the insignificant variations are likely due to a diminished decide to try dimensions, providing suggestive facts towards perception (Supplementary Fig. 7f). Eventually, to try the latest robustness of regression performance, we further managed almost every other covariates as well as book seasons, PI sex, PI battle, establishment reputation while the counted because of the level of successful R01 awards in identical period, and you will PIs’ past NIH feel. I recovered a comparable efficiency (Additional Fig. 17).
Coarsened right coordinating
To help eliminate the effectation of observable products and you can consolidate this new robustness of your overall performance, we operating the state-of-artwork strategy, i.age., Coarsened Precise Coordinating (CEM) 61 . This new matching method next guarantees this new resemblance between slim victories and you can close misses ex ante. The fresh CEM formula relates to around three procedures:
Prune from the analysis set brand new gadgets in virtually any stratum one don’t were one or more treated and something manage product.
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).