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).
Together with her, such overall performance examine the main presumptions of your own 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 one’s examination procedure, i employ a conventional removing approach while the described in the primary text message (Fig. 3b) and you will upgrade the complete regression study. I recover once again a life threatening effect of early-community drawback to your opportunities to share struck papers and you will average citations (Second Fig. 7d, e). Getting hits for every single capita, we discover the effect of the same advice, and also the insignificant distinctions are probably on account of a lower decide to try size, offering suggestive proof on the impact (Additional Fig. 7f). Eventually, in order to take to brand new robustness of regression show, we further managed other covariates and additionally publication season, PI sex, PI competition, establishment profile just like the measured because of the number of winning R01 prizes in the same period, and you may PIs’ past NIH feel. We recovered the same performance (Supplementary Fig. 17).
Coarsened perfect coordinating
To further eliminate the effect of observable affairs and you can combine the newest robustness of one’s efficiency, i functioning the official-of-artwork strategy, we.elizabeth., Coarsened Specific Complimentary (CEM) 61 . The newest matching method next assures the new resemblance between thin gains and you can close misses ex ante. The brand new CEM formula relates to around three actions:
Prune throughout the research put the fresh new products in just about any stratum you to definitely do not are a minumum of one managed and another manage device.
Following the algorithm, we use a set of hoe feabie-account te verwijderen 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).