Code and data from: Differential retention contributes to racial/ethnic disparity in U.S. academia



This contains model code and data from the paper titled
  "Differential retention contributes to racial/ethnic disparity in U.S. academia"
  By: Allison K. Shaw, Chiara Accolla, Jeremy M. Chacón, Taryn L. Mueller, Maxime Vaugeois, Ya Yang, Nitin Sekar, Daniel E. Stanton
  Submitted to: PLoS ONE
  Abstract: Several racial and ethnic identities are widely understood to be under-represented within academia, however, actual quantification of this under-representation is surprisingly limited. Challenges include data availability, demographic inertia and identifying comparison points. We use de-aggregated data from the U.S. National Science Foundation to construct a null model of ethnic and racial representation in one of the world’s largest academic communities. Making comparisons between our model and actual representation in academia allows us to measure the effects of retention (while controlling for recruitment) at different academic stages. We find that, regardless of recruitment, failed retention contributes to mis-representation across academia and that the stages responsible for the largest disparities differ by race and ethnicity: for Black and Hispanic scholars this occurs at the transition from graduate student to postdoctoral researcher whereas for Native American/Alaskan Native and Native Hawaiian/Pacific Islander scholars this occurs at transitions to and within faculty stages. Even for Asian and Asian-Americans, often perceived as well represented, circumstances are complex and depend on choice of baseline. Our findings demonstrate that while recruitment continues to be important, retention is also a pervasive barrier to proportional representation. Therefore, strategies to reduce mis-representation in academia must address retention. Although our model does not directly suggest such strategies, our framework could be used to project how representation in academia might change in the long-term under different scenarios.
Date made available2021

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